Infrared imaging enhancement with fusion

ABSTRACT

Techniques using small form factor infrared imaging modules are disclosed. An imaging system may include visible spectrum imaging modules, infrared imaging modules, and other modules to interface with a user and/or a monitoring system. Visible spectrum imaging modules and infrared imaging modules may be positioned in proximity to a scene that will be monitored while visible spectrum-only images of the scene are either not available or less desirable than infrared images of the scene. Imaging modules may be configured to capture images of the scene at different times. Image analytics and processing may be used to generate combined images with infrared imaging features and increased detail and contrast. Triple fusion processing, including selectable aspects of non-uniformity correction processing, true color processing, and high contrast processing, may be performed on the captured images. Control signals based on the combined images may be presented to a user and/or a monitoring system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/792,582 filed Mar. 15, 2013 and entitled “TIME SPACEDINFRARED IMAGE ENHANCEMENT” which is hereby incorporated by reference inits entirety.

This application claims the benefit of U.S. Provisional PatentApplication No. 61/793,952 filed Mar. 15, 2013 and entitled “INFRAREDIMAGING ENHANCEMENT WITH FUSION” which is hereby incorporated byreference in its entirety.

This application claims the benefit of U.S. Provisional PatentApplication No. 61/746,069 filed Dec. 26, 2012 and entitled “TIME SPACEDINFRARED IMAGE ENHANCEMENT” which is hereby incorporated by reference inits entirety.

This application claims the benefit of U.S. Provisional PatentApplication No. 61/746,074 filed Dec. 26, 2012 and entitled “INFRAREDIMAGING ENHANCEMENT WITH FUSION” which is hereby incorporated byreference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/101,245 filed Dec. 9, 2013 and entitled “LOW POWER AND SMALLFORM FACTOR INFRARED IMAGING” which is hereby incorporated by referencein its entirety.

U.S. patent application Ser. No. 14/101,245 is a continuation ofInternational Patent Application No. PCT/US2012/041744 filed Jun. 8,2012 and entitled “LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/656,889 filed Jun.7, 2012 and entitled “LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/545,056 filed Oct.7, 2011 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRAREDIMAGING DEVICES” which is hereby incorporated by reference in itsentirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun.10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun.10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims thebenefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun.10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which ishereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/099,818 filed Dec. 6, 2013 and entitled “NON-UNIFORMITYCORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES” which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/099,818 is a continuation ofInternational Patent Application No. PCT/US2012/041749 filed Jun. 8,2012 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRAREDIMAGING DEVICES” which is hereby incorporated by reference in itsentirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/545,056 filed Oct.7, 2011 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRAREDIMAGING DEVICES” which is hereby incorporated by reference in itsentirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun.10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS”which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun.10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041749 claims thebenefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun.10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which ishereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/101,258 filed Dec. 9, 2013 and entitled “INFRARED CAMERASYSTEM ARCHITECTURES” which is hereby incorporated by reference in itsentirety.

U.S. patent application Ser. No. 14/101,258 is a continuation ofInternational Patent Application No. PCT/US2012/041739 filed Jun. 8,2012 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which is herebyincorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims thebenefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun.10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS”which is hereby incorporated by reference in its entirety,

International Patent Application No. PCT/US2012/041739 claims thebenefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun.10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which ishereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims thebenefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun.10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which ishereby incorporated by reference in its entirety.

This patent application is a continuation-in-part of U.S. patentapplication Ser. No. 13/437,645 filed Apr. 2, 2012 and entitled“INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION” which ishereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 13/437,645 is a continuation-in-part ofU.S. patent application Ser. No. 13/105,765 filed May 11, 2011 andentitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION”which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 13/437,645 also claims the benefit ofU.S. Provisional Patent Application No. 61/473,207 filed Apr. 8, 2011and entitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION”which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 13/437,645 is also acontinuation-in-part of U.S. patent application Ser. No. 12/766,739filed Apr. 23, 2010 and entitled “INFRARED RESOLUTION AND CONTRASTENHANCEMENT WITH FUSION” which is hereby incorporated by reference inits entirety.

U.S. patent application Ser. No. 13/105,765 is a continuation ofInternational Patent Application No. PCT/EP2011/056432 filed Apr. 21,2011 and entitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITHFUSION” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 13/105,765 is also acontinuation-in-part of U.S. patent application Ser. No. 12/766,739which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/EP2011/056432 is acontinuation-in-part of U.S. patent application Ser. No. 12/766,739which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/EP2011/056432 also claims thebenefit of U.S. Provisional Patent Application No. 61/473,207 which ishereby incorporated by reference in its entirety.

This application claims the benefit of U.S. Provisional PatentApplication No. 61/748,018 filed Dec. 31, 2012 and entitled “COMPACTMULTI-SPECTRUM IMAGING WITH FUSION” which is hereby incorporated byreference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/477,828 filed Jun. 3, 2009 and entitled “INFRARED CAMERASYSTEMS AND METHODS FOR DUAL SENSOR APPLICATIONS” which is herebyincorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/029,683 filed Sep. 17, 2013 and entitled “PIXEL-WISE NOISEREDUCTION IN THERMAL IMAGES”, which is hereby incorporated by referencein its entirety.

U.S. patent application Ser. No. 14/029,683 claims the benefit of U.S.Provisional Patent Application No. 61/745,489 filed Dec. 21, 2012 andentitled “ROW AND COLUMN NOISE REDUCTION IN THERMAL IMAGES”, which ishereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,683 claims the benefit of U.S.Provisional Patent Application No. 61/745,504 filed Dec. 21, 2012 andentitled “PIXEL-WISE NOISE REDUCTION IN THERMAL IMAGES”, which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,683 is a continuation-in-part ofU.S. patent application Ser. No. 13/622,178 filed Sep. 18, 2012 andentitled “SYSTEMS AND METHODS FOR PROCESSING INFRARED IMAGES”, which isa continuation-in-part of U.S. patent application Ser. No. 13/529,772filed Jun. 21, 2012 and entitled “SYSTEMS AND METHODS FOR PROCESSINGINFRARED IMAGES”, which is a continuation of U.S. patent applicationSer. No. 12/396,340 filed Mar. 2, 2009 and entitled “SYSTEMS AND METHODSFOR PROCESSING INFRARED IMAGES”, all of which are hereby incorporated byreference in their entirety.

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/029,716 filed Sep. 17, 2013 and entitled “ROW AND COLUMNNOISE REDUCTION IN THERMAL IMAGES”, which is hereby incorporated byreference in its entirety.

U.S. patent application Ser. No. 14/029,716 claims the benefit of U.S.Provisional Patent Application No. 61/745,489 filed Dec. 21, 2012 andentitled “ROW AND COLUMN NOISE REDUCTION IN THERMAL IMAGES”, which ishereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,716 claims the benefit of U.S.Provisional Patent Application No. 61/745,504 filed Dec. 21, 2012 andentitled “PIXEL-WISE NOISE REDUCTION IN THERMAL IMAGES”, which is herebyincorporated by reference in its entirety.

U.S. patent application Ser. No. 14/029,716 is a continuation-in-part ofU.S. patent application Ser. No. 13/622,178 filed Sep. 18, 2012 andentitled “SYSTEMS AND METHODS FOR PROCESSING INFRARED IMAGES”, which isa continuation-in-part of U.S. patent application Ser. No. 13/529,772filed Jun. 21, 2012 and entitled “SYSTEMS AND METHODS FOR PROCESSINGINFRARED IMAGES”, which is a continuation of U.S. patent applicationSer. No. 12/396,340 filed Mar. 2, 2009 and entitled “SYSTEMS AND METHODSFOR PROCESSING INFRARED IMAGES”, all of which are hereby incorporated byreference in their entirety.

TECHNICAL FIELD

One or more embodiments of the invention relate generally to infraredimaging devices and more particularly, for example, to systems andmethods for enhanced imaging using infrared imaging devices.

BACKGROUND

Visible spectrum cameras are used in a variety of imaging applicationsto capture color or monochrome images derived from visible light.Visible spectrum cameras are often used for daytime or otherapplications when there is sufficient ambient light or when imagedetails are not obscured by smoke, fog, or other environmentalconditions detrimentally affecting the visible spectrum.

Infrared cameras are used in a variety of imaging applications tocapture infrared (e.g., thermal) emissions from objects as infraredimages. Infrared cameras may be used for nighttime or other applicationswhen ambient lighting is poor or when environmental conditions areotherwise non-conducive to visible spectrum imaging. Infrared camerasmay also be used for applications in which additionalnon-visible-spectrum information about a scene is desired. Conventionalinfrared cameras typically produce infrared images that are difficult tointerpret due to, for example, lack of resolution, lack of contrastbetween objects, and excess noise.

SUMMARY

Techniques are disclosed for systems and methods using small form factorinfrared imaging modules to image a scene. In one embodiment, an imagingsystem may include one or more visible spectrum imaging modules andinfrared imaging modules, a processor, a memory, a display, acommunication module, and modules to interface with a user and/or amonitoring and notification system. Visible spectrum imaging modules andinfrared imaging modules may be positioned in proximity to a scene thatwill be monitored while a visible spectrum-only image of the scene iseither not available or less desirable than an infrared image of thescene.

The visible spectrum imaging modules may be configured to capturevisible spectrum images of the scene at a first time, and the infraredimaging modules may be configured to capture infrared images of thescene at a second time. The second time may be substantially differentfrom the first time, or the times may be substantially simultaneous.Various image analytics and processing may be performed on the capturedimages to form combined images with infrared imaging features andincreased available detail and contrast.

In one embodiment, triple fusion processing, including selectableaspects of non-uniformity correction processing, true color processing,and high contrast processing may be performed on the captured images.Notifications and control signals may be generated based on the combinedimages and then presented to a user and/or a monitoring and notificationsystem.

In another embodiment, a system includes a memory adapted to receive avisible spectrum image of a scene and an infrared image of the scene,and a processor in communication with the memory. The processor may beconfigured to receive control parameters, derive color characteristicsof the scene from at least one of the images, and derive high spatialfrequency content from at least one of the images. In some embodiments,the images used derive the color characteristics and the high spatialfrequency may or may not be the same images and/or types of images. Theprocessor may be configured to generate a combined image comprisingrelative contributions of the color characteristics and the high spatialfrequency content, where the relative contributions may be determined bythe control parameters.

In a further embodiment, a method includes receiving a visible spectrumimage of a scene and an infrared image of the scene, receiving controlparameters, deriving color characteristics of the scene from at leastone of the images, and deriving high spatial frequency content from atleast one of the images. In some embodiments, the images used derive thecolor characteristics and the high spatial frequency may or may not bethe same images and/or types of images. The method may includegenerating a combined image comprising relative contributions of thecolor characteristics and the high spatial frequency content, where therelative contributions may be determined by the control parameters.

Another embodiment may include a non-transitory machine-readable mediumhaving a plurality of machine-readable instructions which when executedby one or more processors of an imaging system are adapted to cause theimaging system to perform a method for imaging a scene. The method mayinclude receiving a visible spectrum image of a scene and an infraredimage of the scene, receiving control parameters, deriving colorcharacteristics of the scene from at least one of the images, andderiving high spatial frequency content from at least one of the images.In some embodiments, the images used derive the color characteristicsand the high spatial frequency may or may not be the same images and/ortypes of images. The method may include generating a combined imagecomprising relative contributions of the color characteristics and thehigh spatial frequency content, where the relative contributions may bedetermined by the control parameters.

In another embodiment, a system includes a visible spectrum imagingmodule configured to capture a visible spectrum image of a scene at afirst time, and an infrared imaging module configured to capture aninfrared image of the scene at a second time, where the infrared imageincludes a radiometric component. A processor in communication with thevisible spectrum imaging module and the infrared imaging module may beconfigured to process the visible spectrum image and the infrared imageto generate a combined image comprising visible spectrum characteristicsof the scene derived from the visible spectrum image and infraredcharacteristics of the scene derived from the radiometric component ofthe infrared image.

In a further embodiment, a method includes receiving a visible spectrumimage of a scene captured at a first time by a visible spectrum imagingmodule, and receiving an infrared image of the scene captured at asecond time by an infrared imaging module, where the infrared imagecomprises a radiometric component. The method may further includeprocessing the visible spectrum image and the infrared image to generatea combined image comprising visible spectrum characteristics of thescene derived from the visible spectrum image and infraredcharacteristics of the scene derived from the radiometric component ofthe infrared image.

Another embodiment may include a non-transitory machine-readable mediumhaving a plurality of machine-readable instructions which when executedby one or more processors of an imaging system are adapted to cause theimaging system to perform a method for imaging a scene. The method mayinclude receiving a visible spectrum image of a scene captured at afirst time by a visible spectrum imaging module, and receiving aninfrared image of the scene captured at a second time by an infraredimaging module, where the infrared image comprises a radiometriccomponent. The method may further include processing the visiblespectrum image and the infrared image to generate a combined imagecomprising visible spectrum characteristics of the scene derived fromthe visible spectrum image and infrared characteristics of the scenederived from the radiometric component of the infrared image.

The scope of the invention is defined by the claims, which areincorporated into this section by reference. A more completeunderstanding of embodiments of the invention will be afforded to thoseskilled in the art, as well as a realization of additional advantagesthereof, by a consideration of the following detailed description of oneor more embodiments. Reference will be made to the appended sheets ofdrawings that will first be described briefly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an infrared imaging module configured to beimplemented in a host device in accordance with an embodiment of thedisclosure.

FIG. 2 illustrates an assembled infrared imaging module in accordancewith an embodiment of the disclosure.

FIG. 3 illustrates an exploded view of an infrared imaging modulejuxtaposed over a socket in accordance with an embodiment of thedisclosure.

FIG. 4 illustrates a block diagram of an infrared sensor assemblyincluding an array of infrared sensors in accordance with an embodimentof the disclosure.

FIG. 5 illustrates a flow diagram of various operations to determinenon-uniformity correction (NUC) terms in accordance with an embodimentof the disclosure.

FIG. 6 illustrates differences between neighboring pixels in accordancewith an embodiment of the disclosure.

FIG. 7 illustrates a flat field correction technique in accordance withan embodiment of the disclosure.

FIG. 8 illustrates various image processing techniques of FIG. 5 andother operations applied in an image processing pipeline in accordancewith an embodiment of the disclosure.

FIG. 9 illustrates a temporal noise reduction process in accordance withan embodiment of the disclosure.

FIG. 10 illustrates particular implementation details of severalprocesses of the image processing pipeline of FIG. 8 in accordance withan embodiment of the disclosure.

FIG. 11 illustrates spatially correlated fixed pattern noise (FPN) in aneighborhood of pixels in accordance with an embodiment of thedisclosure.

FIG. 12 illustrates a block diagram of another implementation of aninfrared sensor assembly including an array of infrared sensors and alow-dropout regulator in accordance with an embodiment of thedisclosure.

FIG. 13 illustrates a circuit diagram of a portion of the infraredsensor assembly of FIG. 12 in accordance with an embodiment of thedisclosure.

FIG. 14 shows a block diagram of a system for infrared image processingin accordance with an embodiment of the disclosure.

FIGS. 15A-C are flowcharts illustrating methods for noise filtering aninfrared image in accordance with embodiments of the disclosure.

FIGS. 16A-C are graphs illustrating infrared image data and theprocessing of an infrared image in accordance with embodiments of thedisclosure.

FIG. 17 shows a portion of a row of sensor data for discussingprocessing techniques in accordance with embodiments of the disclosure.

FIGS. 18A-C show an exemplary implementation of column and row noisefiltering for an infrared image in accordance with embodiments of thedisclosure.

FIG. 19A shows an infrared image of a scene including small verticalstructure in accordance with an embodiment of the disclosure.

FIG. 19B shows a corrected version of the infrared image of FIG. 19A inaccordance with an embodiment of the disclosure.

FIG. 20A shows an infrared image of a scene including a large verticalstructure in accordance with an embodiment of the disclosure.

FIG. 20B shows a corrected version of the infrared image of FIG. 20A inaccordance with an embodiment of the disclosure.

FIG. 21 is a flowchart illustrating another method for noise filteringan infrared image in accordance with an embodiment of the disclosure,

FIG. 22A shows a histogram prepared for the infrared image of FIG. 19Ain accordance with an embodiment of the disclosure.

FIG. 22B shows a histogram prepared for the infrared image of FIG. 20Ain accordance with an embodiment of the disclosure.

FIG. 23A illustrates an infrared image of a scene, in accordance with anembodiment of the disclosure.

FIG. 23B is a flowchart illustrating still another method for noisefiltering an infrared image in accordance with an embodiment of thedisclosure.

FIGS. 23C-E show histograms prepared for neighborhoods around selectedpixels of the infrared image of FIG. 23A in accordance with embodimentsof the disclosure.

FIG. 24 illustrates a block diagram of an imaging system adapted toimage a scene in accordance with an embodiment of the disclosure.

FIG. 25 illustrates a flow diagram of various operations to enhanceinfrared imaging of a scene in accordance with an embodiment of thedisclosure.

FIG. 26 illustrates a flow diagram of various operations to enhanceinfrared imaging of a scene in accordance with an embodiment of thedisclosure.

FIG. 27 illustrates a flow diagram of various operations to enhanceinfrared imaging of a scene in accordance with an embodiment of thedisclosure.

FIG. 28 illustrates a user interface for an imaging system adapted toimage a scene in accordance with an embodiment of the disclosure.

FIG. 29 illustrates an infrared image in accordance with an embodimentof the disclosure.

FIG. 30 illustrates the infrared image of FIG. 29 after low passfiltering in accordance with an embodiment of the disclosure.

FIG. 31 illustrates high spatial frequency content derived from avisible spectrum image using high pass filtering in accordance with anembodiment of the disclosure.

FIG. 32 illustrates a combination of the low pass filtered infraredimage of FIG. 30 with the high pass filtered visible spectrum image ofFIG. 31 generated in accordance with an embodiment of the disclosure.

FIG. 33 illustrates a low resolution infrared image of a scene inaccordance with an embodiment of the disclosure.

FIG. 34 illustrates the infrared image of FIG. 33 after being resampled,processed, and combined with high spatial frequency content derived froma visible spectrum image of the scene in accordance with an embodimentof the disclosure.

FIG. 35 illustrates a combined image generated in accordance with anembodiment of the disclosure.

FIG. 36 illustrates scaling of a portion of an infrared image and aresulting combined image generated in accordance with an embodiment ofthe disclosure.

Embodiments of the invention and their advantages are best understood byreferring to the detailed description that follows. It should beappreciated that like reference numerals are used to identify likeelements illustrated in one or more of the figures.

DETAILED DESCRIPTION

FIG. 1 illustrates an infrared imaging module 100 (e.g., an infraredcamera or an infrared imaging device) configured to be implemented in ahost device 102 in accordance with an embodiment of the disclosure.Infrared imaging module 100 may be implemented, for one or moreembodiments, with a small form factor and in accordance with wafer levelpackaging techniques or other packaging techniques.

In one embodiment, infrared imaging module 100 may be configured to beimplemented in a small portable host device 102, such as a mobiletelephone, a tablet computing device, a laptop computing device, apersonal digital assistant, a visible light camera, a music player, orany other appropriate mobile device. In this regard, infrared imagingmodule 100 may be used to provide infrared imaging features to hostdevice 102. For example, infrared imaging module 100 may be configuredto capture, process, and/or otherwise manage infrared images (e.g., alsoreferred to as image frames) and provide such infrared images to hostdevice 102 for use in any desired fashion (e.g., for further processing,to store in memory, to display, to use by various applications runningon host device 102, to export to other devices, or other uses).

In various embodiments, infrared imaging module 100 may be configured tooperate at low voltage levels and over a wide temperature range. Forexample, in one embodiment, infrared imaging module 100 may operateusing a power supply of approximately 2.4 volts, 2.5 volts, 2.8 volts,or lower voltages, and operate over a temperature range of approximately−20 degrees C. to approximately +60 degrees C. (e.g., providing asuitable dynamic range and performance over an environmental temperaturerange of approximately 80 degrees C.). In one embodiment, by operatinginfrared imaging module 100 at low voltage levels, infrared imagingmodule 100 may experience reduced amounts of self heating in comparisonwith other types of infrared imaging devices. As a result, infraredimaging module 100 may be operated with reduced measures to compensatefor such self heating.

As shown in FIG. 1, host device 102 may include a socket 104, a shutter105, motion sensors 194, a processor 195, a memory 196, a display 197,and/or other components 198. Socket 104 may be configured to receiveinfrared imaging module 100 as identified by arrow 101. In this regard,FIG. 2 illustrates infrared imaging module 100 assembled in socket 104in accordance with an embodiment of the disclosure.

Motion sensors 194 may be implemented by one or more accelerometers,gyroscopes, or other appropriate devices that may be used to detectmovement of host device 102. Motion sensors 194 may be monitored by andprovide information to processing module 160 or processor 195 to detectmotion. In various embodiments, motion sensors 194 may be implemented aspart of host device 102 (as shown in FIG. 1), infrared imaging module100, or other devices attached to or otherwise interfaced with hostdevice 102.

Processor 195 may be implemented as any appropriate processing device(e.g., logic device, microcontroller, processor, application specificintegrated circuit (ASIC), or other device) that may be used by hostdevice 102 to execute appropriate instructions, such as softwareinstructions provided in memory 196. Display 197 may be used to displaycaptured and/or processed infrared images and/or other images, data, andinformation. Other components 198 may be used to implement any featuresof host device 102 as may be desired for various applications (e.g.,clocks, temperature sensors, a visible light camera, or othercomponents). In addition, a machine readable medium 193 may be providedfor storing non-transitory instructions for loading into memory 196 andexecution by processor 195.

In various embodiments, infrared imaging module 100 and socket 104 maybe implemented for mass production to facilitate high volumeapplications, such as for implementation in mobile telephones or otherdevices (e.g., requiring small form factors). In one embodiment, thecombination of infrared imaging module 100 and socket 104 may exhibitoverall dimensions of approximately 8.5 mm by 8.5 mm by 5.9 mm whileinfrared imaging module 100 is installed in socket 104.

FIG. 3 illustrates an exploded view of infrared imaging module 100juxtaposed over socket 104 in accordance with an embodiment of thedisclosure. Infrared imaging module 100 may include a lens barrel 110, ahousing 120, an infrared sensor assembly 128, a circuit board 170, abase 150, and a processing module 160.

Lens barrel 110 may at least partially enclose an optical element 180(e.g., a lens) which is partially visible in FIG. 3 through an aperture112 in lens barrel 110. Lens barrel 110 may include a substantiallycylindrical extension 114 which may be used to interface lens barrel 110with an aperture 122 in housing 120.

Infrared sensor assembly 128 may be implemented, for example, with a cap130 (e.g., a lid) mounted on a substrate 140. Infrared sensor assembly128 may include a plurality of infrared sensors 132 (e.g., infrareddetectors) implemented in an array or other fashion on substrate 140 andcovered by cap 130. For example, in one embodiment, infrared sensorassembly 128 may be implemented as a focal plane array (FPA). Such afocal plane array may be implemented, for example, as a vacuum packageassembly (e.g., sealed by cap 130 and substrate 140). In one embodiment,infrared sensor assembly 128 may be implemented as a wafer level package(e.g., infrared sensor assembly 128 may be singulated from a set ofvacuum package assemblies provided on a wafer). In one embodiment,infrared sensor assembly 128 may be implemented to operate using a powersupply of approximately 2.4 volts, 2.5 volts, 2.8 volts, or similarvoltages.

Infrared sensors 132 may be configured to detect infrared radiation(e.g., infrared energy) from a target scene including, for example, midwave infrared wave bands (MWIR), long wave infrared wave bands (LWIR),and/or other thermal imaging bands as may be desired in particularimplementations. In one embodiment, infrared sensor assembly 128 may beprovided in accordance with wafer level packaging techniques.

Infrared sensors 132 may be implemented, for example, as microbolometersor other types of thermal imaging infrared sensors arranged in anydesired array pattern to provide a plurality of pixels. In oneembodiment, infrared sensors 132 may be implemented as vanadium oxide(VOx) detectors with a 17 μm pixel pitch. In various embodiments, arraysof approximately 32 by 32 infrared sensors 132, approximately 64 by 64infrared sensors 132, approximately 80 by 64 infrared sensors 132, orother array sizes may be used.

Substrate 140 may include various circuitry including, for example, aread out integrated circuit (ROIC) with dimensions less thanapproximately 5.5 mm by 5.5 mm in one embodiment. Substrate 140 may alsoinclude bond pads 142 that may be used to contact complementaryconnections positioned on inside surfaces of housing 120 when infraredimaging module 100 is assembled as shown in FIG. 3. In one embodiment,the ROIC may be implemented with low-dropout regulators (LDO) to performvoltage regulation to reduce power supply noise introduced to infraredsensor assembly 128 and thus provide an improved power supply rejectionratio (PSRR). Moreover, by implementing the LDO with the ROIC (e.g.,within a wafer level package), less die area may be consumed and fewerdiscrete die (or chips) are needed.

FIG. 4 illustrates a block diagram of infrared sensor assembly 128including an array of infrared sensors 132 in accordance with anembodiment of the disclosure. In the illustrated embodiment, infraredsensors 132 are provided as part of a unit cell array of a ROIC 402.ROIC 402 includes bias generation and timing control circuitry 404,column amplifiers 405, a column multiplexer 406, a row multiplexer 408,and an output amplifier 410. Image frames (e.g., thermal images)captured by infrared sensors 132 may be provided by output amplifier 410to processing module 160, processor 195, and/or any other appropriatecomponents to perform various processing techniques described herein.Although an 8 by 8 array is shown in FIG. 4, any desired arrayconfiguration may be used in other embodiments. Further descriptions ofROICs and infrared sensors (e.g., microbolometer circuits) may be foundin U.S. Pat. No. 6,028,309 issued Feb. 22, 2000, which is incorporatedherein by reference in its entirety.

Infrared sensor assembly 128 may capture images (e.g., image frames) andprovide such images from its ROIC at various rates. Processing module160 may be used to perform appropriate processing of captured infraredimages and may be implemented in accordance with any appropriatearchitecture. In one embodiment, processing module 160 may beimplemented as an ASIC. In this regard, such an ASIC may be configuredto perform image processing with high performance and/or highefficiency. In another embodiment, processing module 160 may beimplemented with a general purpose central processing unit (CPU) whichmay be configured to execute appropriate software instructions toperform image processing, coordinate and perform image processing withvarious image processing blocks, coordinate interfacing betweenprocessing module 160 and host device 102, and/or other operations. Inyet another embodiment, processing module 160 may be implemented with afield programmable gate array (FPGA). Processing module 160 may beimplemented with other types of processing and/or logic circuits inother embodiments as would be understood by one skilled in the art.

In these and other embodiments, processing module 160 may also beimplemented with other components where appropriate, such as, volatilememory, non-volatile memory, and/or one or more interfaces (e.g.,infrared detector interfaces, inter-integrated circuit (I2C) interfaces,mobile industry processor interfaces (MIPI), joint test action group(JTAG) interfaces (e.g., IEEE 1149.1 standard test access port andboundary-scan architecture), and/or other interfaces).

In some embodiments, infrared imaging module 100 may further include oneor more actuators 199 which may be used to adjust the focus of infraredimage frames captured by infrared sensor assembly 128. For example,actuators 199 may be used to move optical element 180, infrared sensors132, and/or other components relative to each other to selectively focusand defocus infrared image frames in accordance with techniquesdescribed herein. Actuators 199 may be implemented in accordance withany type of motion-inducing apparatus or mechanism, and may positionedat any location within or external to infrared imaging module 100 asappropriate for different applications.

When infrared imaging module 100 is assembled, housing 120 maysubstantially enclose infrared sensor assembly 128, base 150, andprocessing module 160. Housing 120 may facilitate connection of variouscomponents of infrared imaging module 100. For example, in oneembodiment, housing 120 may provide electrical connections 126 toconnect various components as further described.

Electrical connections 126 (e.g., conductive electrical paths, traces,or other types of connections) may be electrically connected with bondpads 142 when infrared imaging module 100 is assembled. In variousembodiments, electrical connections 126 may be embedded in housing 120,provided on inside surfaces of housing 120, and/or otherwise provided byhousing 120. Electrical connections 126 may terminate in connections 124protruding from the bottom surface of housing 120 as shown in FIG. 3.Connections 124 may connect with circuit board 170 when infrared imagingmodule 100 is assembled (e.g., housing 120 may rest atop circuit board170 in various embodiments). Processing module 160 may be electricallyconnected with circuit board 170 through appropriate electricalconnections. As a result, infrared sensor assembly 128 may beelectrically connected with processing module 160 through, for example,conductive electrical paths provided by: bond pads 142, complementaryconnections on inside surfaces of housing 120, electrical connections126 of housing 120, connections 124, and circuit board 170.Advantageously, such an arrangement may be implemented without requiringwire bonds to be provided between infrared sensor assembly 128 andprocessing module 160.

In various embodiments, electrical connections 126 in housing 120 may bemade from any desired material (e.g., copper or any other appropriateconductive material). In one embodiment, electrical connections 126 mayaid in dissipating heat from infrared imaging module 100.

Other connections may be used in other embodiments. For example, in oneembodiment, sensor assembly 128 may be attached to processing module 160through a ceramic board that connects to sensor assembly 128 by wirebonds and to processing module 160 by a ball grid array (BGA). Inanother embodiment, sensor assembly 128 may be mounted directly on arigid flexible board and electrically connected with wire bonds, andprocessing module 160 may be mounted and connected to the rigid flexibleboard with wire bonds or a BGA.

The various implementations of infrared imaging module 100 and hostdevice 102 set forth herein are provided for purposes of example, ratherthan limitation. In this regard, any of the various techniques describedherein may be applied to any infrared camera system, infrared imager, orother device for performing infrared/thermal imaging.

Substrate 140 of infrared sensor assembly 128 may be mounted on base150. In various embodiments, base 150 (e.g., a pedestal) may be made,for example, of copper formed by metal injection molding (MIM) andprovided with a black oxide or nickel-coated finish. In variousembodiments, base 150 may be made of any desired material, such as forexample zinc, aluminum, or magnesium, as desired for a given applicationand may be formed by any desired applicable process, such as for examplealuminum casting, MIM, or zinc rapid casting, as may be desired forparticular applications. In various embodiments, base 150 may beimplemented to provide structural support, various circuit paths,thermal heat sink properties, and other features where appropriate. Inone embodiment, base 150 may be a multi-layer structure implemented atleast in part using ceramic material.

In various embodiments, circuit board 170 may receive housing 120 andthus may physically support the various components of infrared imagingmodule 100. In various embodiments, circuit board 170 may be implementedas a printed circuit board (e.g., an FR4 circuit board or other types ofcircuit boards), a rigid or flexible interconnect (e.g., tape or othertype of interconnects), a flexible circuit substrate, a flexible plasticsubstrate, or other appropriate structures. In various embodiments, base150 may be implemented with the various features and attributesdescribed for circuit board 170, and vice versa.

Socket 104 may include a cavity 106 configured to receive infraredimaging module 100 (e.g., as shown in the assembled view of FIG. 2).Infrared imaging module 100 and/or socket 104 may include appropriatetabs, arms, pins, fasteners, or any other appropriate engagement memberswhich may be used to secure infrared imaging module 100 to or withinsocket 104 using friction, tension, adhesion, and/or any otherappropriate manner. Socket 104 may include engagement members 107 thatmay engage surfaces 109 of housing 120 when infrared imaging module 100is inserted into a cavity 106 of socket 104. Other types of engagementmembers may be used in other embodiments.

Infrared imaging module 100 may be electrically connected with socket104 through appropriate electrical connections (e.g., contacts, pins,wires, or any other appropriate connections). For example, socket 104may include electrical connections 108 which may contact correspondingelectrical connections of infrared imaging module 100 (e.g.,interconnect pads, contacts, or other electrical connections on side orbottom surfaces of circuit board 170, bond pads 142 or other electricalconnections on base 150, or other connections). Electrical connections108 may be made from any desired material (e.g., copper or any otherappropriate conductive material). In one embodiment, electricalconnections 108 may be mechanically biased to press against electricalconnections of infrared imaging module 100 when infrared imaging module100 is inserted into cavity 106 of socket 104. In one embodiment,electrical connections 108 may at least partially secure infraredimaging module 100 in socket 104. Other types of electrical connectionsmay be used in other embodiments.

Socket 104 may be electrically connected with host device 102 throughsimilar types of electrical connections. For example, in one embodiment,host device 102 may include electrical connections (e.g., solderedconnections, snap-in connections, or other connections) that connectwith electrical connections 108 passing through apertures 190. Invarious embodiments, such electrical connections may be made to thesides and/or bottom of socket 104.

Various components of infrared imaging module 100 may be implementedwith flip chip technology which may be used to mount components directlyto circuit boards without the additional clearances typically needed forwire bond connections. Flip chip connections may be used, as an example,to reduce the overall size of infrared imaging module 100 for use incompact small form factor applications. For example, in one embodiment,processing module 160 may be mounted to circuit board 170 using flipchip connections. For example, infrared imaging module 100 may beimplemented with such flip chip configurations.

In various embodiments, infrared imaging module 100 and/or associatedcomponents may be implemented in accordance with various techniques(e.g., wafer level packaging techniques) as set forth in U.S. patentapplication Ser. No. 12/844,124 filed Jul. 27, 2010, and U.S.Provisional Patent Application No. 61/469,651 filed Mar. 30, 2011, whichare incorporated herein by reference in their entirety. Furthermore, inaccordance with one or more embodiments, infrared imaging module 100and/or associated components may be implemented, calibrated, tested,and/or used in accordance with various techniques, such as for exampleas set forth in U.S. Pat. No. 7,470,902 issued Dec. 30, 2008, U.S. Pat.No. 6,028,309 issued Feb. 22, 2000, U.S. Pat. No. 6,812,465 issued Nov.2, 2004, U.S. Pat. No. 7,034,301 issued Apr. 25, 2006, U.S. Pat. No.7,679,048 issued Mar. 16, 2010, U.S. Pat. No. 7,470,904 issued Dec. 30,2008, U.S. patent application Ser. No. 12/202,880 filed Sep. 2, 2008,and U.S. patent application Ser. No. 12/202,896 filed Sep. 2, 2008,which are incorporated herein by reference in their entirety.

In some embodiments, host device 102 may include other components 198such as a non-thermal camera (e.g., a visible light camera or other typeof non-thermal imager). The non-thermal camera may be a small formfactor imaging module or imaging device, and may, in some embodiments,be implemented in a manner similar to the various embodiments ofinfrared imaging module 100 disclosed herein, with one or more sensorsand/or sensor arrays responsive to radiation in non-thermal spectrums(e.g., radiation in visible light wavelengths, ultraviolet wavelengths,and/or other non-thermal wavelengths). For example, in some embodiments,the non-thermal camera may be implemented with a charge-coupled device(CCD) sensor, an electron multiplying CCD (EMCCD) sensor, acomplementary metal-oxide-semiconductor (CMOS) sensor, a scientific CMOS(sCMOS) sensor, or other filters and/or sensors.

In some embodiments, the non-thermal camera may be co-located withinfrared imaging module 100 and oriented such that a field-of-view (FOV)of the non-thermal camera at least partially overlaps a FOV of infraredimaging module 100. In one example, infrared imaging module 100 and anon-thermal camera may be implemented as a dual sensor module sharing acommon substrate according to various techniques described in U.S.Provisional Patent Application No. 61/748,018 filed Dec. 31, 2012, whichis incorporated herein by reference.

For embodiments having such a non-thermal light camera, variouscomponents (e.g., processor 195, processing module 160, and/or otherprocessing component) may be configured to superimpose, fuse, blend, orotherwise combine infrared images (e.g., including thermal images)captured by infrared imaging module 100 and non-thermal images (e.g.,including visible light images) captured by a non-thermal camera,whether captured at substantially the same time or different times(e.g., time-spaced over hours, days, daytime versus nighttime, and/orotherwise).

In some embodiments, thermal and non-thermal images may be processed togenerate combined images (e.g., one or more processes performed on suchimages in some embodiments). For example, scene-based NUC processing maybe performed (as further described herein), true color processing may beperformed, and/or high contrast processing may be performed.

Regarding true color processing, thermal images may be blended withnon-thermal images by, for example, blending a radiometric component ofa thermal image with a corresponding component of a non-thermal imageaccording to a blending parameter, which may be adjustable by a userand/or machine in some embodiments. For example, luminance orchrominance components of the thermal and non-thermal images may becombined according to the blending parameter. In one embodiment, suchblending techniques may be referred to as true color infrared imagery.For example, in daytime imaging, a blended image may comprise anon-thermal color image, which includes a luminance component and achrominance component, with its luminance value replaced by theluminance value from a thermal image. The use of the luminance data fromthe thermal image causes the intensity of the true non-thermal colorimage to brighten or dim based on the temperature of the object. Assuch, these blending techniques provide thermal imaging for daytime orvisible light images.

Regarding high contrast processing, high spatial frequency content maybe obtained from one or more of the thermal and non-thermal images(e.g., by performing high pass filtering, difference imaging, and/orother techniques). A combined image may include a radiometric componentof a thermal image and a blended component including infrared (e.g.,thermal) characteristics of a scene blended with the high spatialfrequency content, according to a blending parameter, which may beadjustable by a user and/or machine in some embodiments. In someembodiments, high spatial frequency content from non-thermal images maybe blended with thermal images by superimposing the high spatialfrequency content onto the thermal images, where the high spatialfrequency content replaces or overwrites those portions of the thermalimages corresponding to where the high spatial frequency content exists.For example, the high spatial frequency content may include edges ofobjects depicted in images of a scene, but may not exist within theinterior of such objects. In such embodiments, blended image data maysimply include the high spatial frequency content, which maysubsequently be encoded into one or more components of combined images.

For example, a radiometric component of thermal image may be achrominance component of the thermal image, and the high spatialfrequency content may be derived from the luminance and/or chrominancecomponents of a non-thermal image. In this embodiment, a combined imagemay include the radiometric component (e.g., the chrominance componentof the thermal image) encoded into a chrominance component of thecombined image and the high spatial frequency content directly encoded(e.g., as blended image data but with no thermal image contribution)into a luminance component of the combined image. By doing so, aradiometric calibration of the radiometric component of the thermalimage may be retained. In similar embodiments, blended image data mayinclude the high spatial frequency content added to a luminancecomponent of the thermal images, and the resulting blended data encodedinto a luminance component of resulting combined images.

For example, any of the techniques disclosed in the followingapplications may be used in various embodiments: U.S. patent applicationSer. No. 12/477,828 filed Jun. 3, 2009; U.S. patent application Ser. No.12/766,739 filed Apr. 23, 2010; U.S. patent application Ser. No.13/105,765 filed May 11, 2011; U.S. patent application Ser. No.13/437,645 filed Apr. 2, 2012; U.S. Provisional Patent Application No.61/473,207 filed Apr. 8, 2011; U.S. Provisional Patent Application No.61/746,069 filed Dec. 26, 2012; U.S. Provisional Patent Application No.61/746,074 filed Dec. 26, 2012; U.S. Provisional Patent Application No.61/748,018 filed Dec. 31, 2012; U.S. Provisional Patent Application No.61/792,582 filed Mar. 15, 2013; U.S. Provisional Patent Application No.61/793,952 filed Mar. 15, 2013; and International Patent Application No.PCT/EP2011/056432 filed Apr. 21, 2011, all of such applications areincorporated herein by reference in their entirety. Any of thetechniques described herein, or described in other applications orpatents referenced herein, may be applied to any of the various thermaldevices, non-thermal devices, and uses described herein.

Referring again to FIG. 1, in various embodiments, host device 102 mayinclude shutter 105. In this regard, shutter 105 may be selectivelypositioned over socket 104 (e.g., as identified by arrows 103) whileinfrared imaging module 100 is installed therein. In this regard,shutter 105 may be used, for example, to protect infrared imaging module100 when not in use. Shutter 105 may also be used as a temperaturereference as part of a calibration process (e.g., a NUC process or othercalibration processes) for infrared imaging module 100 as would beunderstood by one skilled in the art.

In various embodiments, shutter 105 may be made from various materialssuch as, for example, polymers, glass, aluminum (e.g., painted oranodized) or other materials. In various embodiments, shutter 105 mayinclude one or more coatings to selectively filter electromagneticradiation and/or adjust various optical properties of shutter 105 (e.g.,a uniform blackbody coating or a reflective gold coating).

In another embodiment, shutter 105 may be fixed in place to protectinfrared imaging module 100 at all times. In this case, shutter 105 or aportion of shutter 105 may be made from appropriate materials (e.g.,polymers or infrared transmitting materials such as silicon, germanium,zinc selenide, or chalcogenide glasses) that do not substantially filterdesired infrared wavelengths. In another embodiment, a shutter may beimplemented as part of infrared imaging module 100 (e.g., within or aspart of a lens barrel or other components of infrared imaging module100), as would be understood by one skilled in the art.

Alternatively, in another embodiment, a shutter (e.g., shutter 105 orother type of external or internal shutter) need not be provided, butrather a NUC process or other type of calibration may be performed usingshutterless techniques. In another embodiment, a NUC process or othertype of calibration using shutterless techniques may be performed incombination with shutter-based techniques.

Infrared imaging module 100 and host device 102 may be implemented inaccordance with any of the various techniques set forth in U.S.Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011, U.S.Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011, andU.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011,which are incorporated herein by reference in their entirety.

In various embodiments, the components of host device 102 and/orinfrared imaging module 100 may be implemented as a local or distributedsystem with components in communication with each other over wiredand/or wireless networks. Accordingly, the various operations identifiedin this disclosure may be performed by local and/or remote components asmay be desired in particular implementations.

FIG. 5 illustrates a flow diagram of various operations to determine NUCterms in accordance with an embodiment of the disclosure. In someembodiments, the operations of FIG. 5 may be performed by processingmodule 160 or processor 195 (both also generally referred to as aprocessor) operating on image frames captured by infrared sensors 132.

In block 505, infrared sensors 132 begin capturing image frames of ascene. Typically, the scene will be the real world environment in whichhost device 102 is currently located. In this regard, shutter 105 (ifoptionally provided) may be opened to permit infrared imaging module toreceive infrared radiation from the scene. Infrared sensors 132 maycontinue capturing image frames during all operations shown in FIG. 5.In this regard, the continuously captured image frames may be used forvarious operations as further discussed. In one embodiment, the capturedimage frames may be temporally filtered (e.g., in accordance with theprocess of block 826 further described herein with regard to FIG. 8) andbe processed by other terms (e.g., factory gain terms 812, factoryoffset terms 816, previously determined NUC terms 817, column FPN terms820, and row FPN terms 824 as further described herein with regard toFIG. 8) before they are used in the operations shown in FIG. 5.

In block 510, a NUC process initiating event is detected. In oneembodiment, the NUC process may be initiated in response to physicalmovement of host device 102. Such movement may be detected, for example,by motion sensors 194 which may be polled by a processor. In oneexample, a user may move host device 102 in a particular manner, such asby intentionally waving host device 102 back and forth in an “erase” or“swipe” movement. In this regard, the user may move host device 102 inaccordance with a predetermined speed and direction (velocity), such asin an up and down, side to side, or other pattern to initiate the NUCprocess. In this example, the use of such movements may permit the userto intuitively operate host device 102 to simulate the “erasing” ofnoise in captured image frames.

In another example, a NUC process may be initiated by host device 102 ifmotion exceeding a threshold value is detected (e.g., motion greaterthan expected for ordinary use). It is contemplated that any desiredtype of spatial translation of host device 102 may be used to initiatethe NUC process.

In yet another example, a NUC process may be initiated by host device102 if a minimum time has elapsed since a previously performed NUCprocess. In a further example, a NUC process may be initiated by hostdevice 102 if infrared imaging module 100 has experienced a minimumtemperature change since a previously performed NUC process. In a stillfurther example, a NUC process may be continuously initiated andrepeated.

In block 515, after a NUC process initiating event is detected, it isdetermined whether the NUC process should actually be performed. In thisregard, the NUC process may be selectively initiated based on whetherone or more additional conditions are met. For example, in oneembodiment, the NUC process may not be performed unless a minimum timehas elapsed since a previously performed NUC process. In anotherembodiment, the NUC process may not be performed unless infrared imagingmodule 100 has experienced a minimum temperature change since apreviously performed NUC process. Other criteria or conditions may beused in other embodiments. If appropriate criteria or conditions havebeen met, then the flow diagram continues to block 520. Otherwise, theflow diagram returns to block 505.

In the NUC process, blurred image frames may be used to determine NUCterms which may be applied to captured image frames to correct for FPN.As discussed, in one embodiment, the blurred image frames may beobtained by accumulating multiple image frames of a moving scene (e.g.,captured while the scene and/or the thermal imager is in motion). Inanother embodiment, the blurred image frames may be obtained bydefocusing an optical element or other component of the thermal imager.

Accordingly, in block 520 a choice of either approach is provided. Ifthe motion-based approach is used, then the flow diagram continues toblock 525. If the defocus-based approach is used, then the flow diagramcontinues to block 530.

Referring now to the motion-based approach, in block 525 motion isdetected. For example, in one embodiment, motion may be detected basedon the image frames captured by infrared sensors 132. In this regard, anappropriate motion detection process (e.g., an image registrationprocess, a frame-to-frame difference calculation, or other appropriateprocess) may be applied to captured image frames to determine whethermotion is present (e.g., whether static or moving image frames have beencaptured). For example, in one embodiment, it can be determined whetherpixels or regions around the pixels of consecutive image frames havechanged more than a user defined amount (e.g., a percentage and/orthreshold value). If at least a given percentage of pixels have changedby at least the user defined amount, then motion will be detected withsufficient certainty to proceed to block 535.

In another embodiment, motion may be determined on a per pixel basis,wherein only pixels that exhibit significant changes are accumulated toprovide the blurred image frame. For example, counters may be providedfor each pixel and used to ensure that the same number of pixel valuesare accumulated for each pixel, or used to average the pixel valuesbased on the number of pixel values actually accumulated for each pixel.Other types of image-based motion detection may be performed such asperforming a Radon transform.

In another embodiment, motion may be detected based on data provided bymotion sensors 194. In one embodiment, such motion detection may includedetecting whether host device 102 is moving along a relatively straighttrajectory through space. For example, if host device 102 is movingalong a relatively straight trajectory, then it is possible that certainobjects appearing in the imaged scene may not be sufficiently blurred(e.g., objects in the scene that may be aligned with or movingsubstantially parallel to the straight trajectory). Thus, in such anembodiment, the motion detected by motion sensors 194 may be conditionedon host device 102 exhibiting, or not exhibiting, particulartrajectories.

In yet another embodiment, both a motion detection process and motionsensors 194 may be used. Thus, using any of these various embodiments, adetermination can be made as to whether or not each image frame wascaptured while at least a portion of the scene and host device 102 werein motion relative to each other (e.g., which may be caused by hostdevice 102 moving relative to the scene, at least a portion of the scenemoving relative to host device 102, or both).

It is expected that the image frames for which motion was detected mayexhibit some secondary blurring of the captured scene (e.g., blurredthermal image data associated with the scene) due to the thermal timeconstants of infrared sensors 132 (e.g., microbolometer thermal timeconstants) interacting with the scene movement.

In block 535, image frames for which motion was detected areaccumulated. For example, if motion is detected for a continuous seriesof image frames, then the image frames of the series may be accumulated.As another example, if motion is detected for only some image frames,then the non-moving image frames may be skipped and not included in theaccumulation. Thus, a continuous or discontinuous set of image framesmay be selected to be accumulated based on the detected motion.

In block 540, the accumulated image frames are averaged to provide ablurred image frame. Because the accumulated image frames were capturedduring motion, it is expected that actual scene information will varybetween the image frames and thus cause the scene information to befurther blurred in the resulting blurred image frame (block 545).

In contrast, FPN (e.g., caused by one or more components of infraredimaging module 100) will remain fixed over at least short periods oftime and over at least limited changes in scene irradiance duringmotion. As a result, image frames captured in close proximity in timeand space during motion will suffer from identical or at least verysimilar FPN. Thus, although scene information may change in consecutiveimage frames, the FPN will stay essentially constant. By averaging,multiple image frames captured during motion will blur the sceneinformation, but will not blur the FPN. As a result, FPN will remainmore clearly defined in the blurred image frame provided in block 545than the scene information.

In one embodiment, 32 or more image frames are accumulated and averagedin blocks 535 and 540. However, any desired number of image frames maybe used in other embodiments, but with generally decreasing correctionaccuracy as frame count is decreased.

Referring now to the defocus-based approach, in block 530, a defocusoperation may be performed to intentionally defocus the image framescaptured by infrared sensors 132. For example, in one embodiment, one ormore actuators 199 may be used to adjust, move, or otherwise translateoptical element 180, infrared sensor assembly 128, and/or othercomponents of infrared imaging module 100 to cause infrared sensors 132to capture a blurred (e.g., unfocused) image frame of the scene. Othernon-actuator based techniques are also contemplated for intentionallydefocusing infrared image frames such as, for example, manual (e.g.,user-initiated) defocusing.

Although the scene may appear blurred in the image frame, FPN (e.g.,caused by one or more components of infrared imaging module 100) willremain unaffected by the defocusing operation. As a result, a blurredimage frame of the scene will be provided (block 545) with FPN remainingmore clearly defined in the blurred image than the scene information.

In the above discussion, the defocus-based approach has been describedwith regard to a single captured image frame. In another embodiment, thedefocus-based approach may include accumulating multiple image frameswhile the infrared imaging module 100 has been defocused and averagingthe defocused image frames to remove the effects of temporal noise andprovide a blurred image frame in block 545.

Thus, it will be appreciated that a blurred image frame may be providedin block 545 by either the motion-based approach or the defocus-basedapproach. Because much of the scene information will be blurred byeither motion, defocusing, or both, the blurred image frame may beeffectively considered a low pass filtered version of the originalcaptured image frames with respect to scene information.

In block 550, the blurred image frame is processed to determine updatedrow and column FPN terms (e.g., if row and column FPN terms have notbeen previously determined then the updated row and column FPN terms maybe new row and column FPN terms in the first iteration of block 550). Asused in this disclosure, the terms row and column may be usedinterchangeably depending on the orientation of infrared sensors 132and/or other components of infrared imaging module 100.

In one embodiment, block 550 includes determining a spatial FPNcorrection term for each row of the blurred image frame (e.g., each rowmay have its own spatial FPN correction term), and also determining aspatial FPN correction term for each column of the blurred image frame(e.g., each column may have its own spatial FPN correction term). Suchprocessing may be used to reduce the spatial and slowly varying (1/f)row and column FPN inherent in thermal imagers caused by, for example,1/f noise characteristics of amplifiers in ROIC 402 which may manifestas vertical and horizontal stripes in image frames.

Advantageously, by determining spatial row and column FPN terms usingthe blurred image frame, there will be a reduced risk of vertical andhorizontal objects in the actual imaged scene from being mistaken forrow and column noise (e.g., real scene content will be blurred while FPNremains unblurred).

In one embodiment, row and column FPN terms may be determined byconsidering differences between neighboring pixels of the blurred imageframe. For example, FIG. 6 illustrates differences between neighboringpixels in accordance with an embodiment of the disclosure. Specifically,in FIG. 6 a pixel 610 is compared to its 8 nearest horizontal neighbors:d0-d3 on one side and d4-d7 on the other side. Differences between theneighbor pixels can be averaged to obtain an estimate of the offseterror of the illustrated group of pixels. An offset error may becalculated for each pixel in a row or column and the average result maybe used to correct the entire row or column.

To prevent real scene data from being interpreted as noise, upper andlower threshold values may be used (thPix and −thPix). Pixel valuesfalling outside these threshold values (pixels dl and d4 in thisexample) are not used to obtain the offset error. In addition, themaximum amount of row and column FPN correction may be limited by thesethreshold values.

Further techniques for performing spatial row and column FPN correctionprocessing are set forth in U.S. patent application Ser. No. 12/396,340filed Mar. 2, 2009 which is incorporated herein by reference in itsentirety.

Referring again to FIG. 5, the updated row and column FPN termsdetermined in block 550 are stored (block 552) and applied (block 555)to the blurred image frame provided in block 545. After these terms areapplied, some of the spatial row and column FPN in the blurred imageframe may be reduced. However, because such terms are applied generallyto rows and columns, additional FPN may remain such as spatiallyuncorrelated FPN associated with pixel to pixel drift or other causes.Neighborhoods of spatially correlated FPN may also remain which may notbe directly associated with individual rows and columns. Accordingly,further processing may be performed as discussed below to determine NUCterms.

In block 560, local contrast values (e.g., edges or absolute values ofgradients between adjacent or small groups of pixels) in the blurredimage frame are determined. If scene information in the blurred imageframe includes contrasting areas that have not been significantlyblurred (e.g., high contrast edges in the original scene data), thensuch features may be identified by a contrast determination process inblock 560.

For example, local contrast values in the blurred image frame may becalculated, or any other desired type of edge detection process may beapplied to identify certain pixels in the blurred image as being part ofan area of local contrast. Pixels that are marked in this manner may beconsidered as containing excessive high spatial frequency sceneinformation that would be interpreted as FPN (e.g., such regions maycorrespond to portions of the scene that have not been sufficientlyblurred). As such, these pixels may be excluded from being used in thefurther determination of NUC terms. In one embodiment, such contrastdetection processing may rely on a threshold that is higher than theexpected contrast value associated with FPN (e.g., pixels exhibiting acontrast value higher than the threshold may be considered to be sceneinformation, and those lower than the threshold may be considered to beexhibiting FPN).

In one embodiment, the contrast determination of block 560 may beperformed on the blurred image frame after row and column FPN terms havebeen applied to the blurred image frame (e.g., as shown in FIG. 5). Inanother embodiment, block 560 may be performed prior to block 550 todetermine contrast before row and column FPN terms are determined (e.g.,to prevent scene based contrast from contributing to the determinationof such terms).

Following block 560, it is expected that any high spatial frequencycontent remaining in the blurred image frame may be generally attributedto spatially uncorrelated FPN. In this regard, following block 560, muchof the other noise or actual desired scene based information has beenremoved or excluded from the blurred image frame due to: intentionalblurring of the image frame (e.g., by motion or defocusing in blocks 520through 545), application of row and column FPN terms (block 555), andcontrast determination (block 560).

Thus, it can be expected that following block 560, any remaining highspatial frequency content (e.g., exhibited as areas of contrast ordifferences in the blurred image frame) may be attributed to spatiallyuncorrelated FPN. Accordingly, in block 565, the blurred image frame ishigh pass filtered. In one embodiment, this may include applying a highpass filter to extract the high spatial frequency content from theblurred image frame. In another embodiment, this may include applying alow pass filter to the blurred image frame and taking a differencebetween the low pass filtered image frame and the unfiltered blurredimage frame to obtain the high spatial frequency content. In accordancewith various embodiments of the present disclosure, a high pass filtermay be implemented by calculating a mean difference between a sensorsignal (e.g., a pixel value) and its neighbors.

In block 570, a flat field correction process is performed on the highpass filtered blurred image frame to determine updated NUC terms (e.g.,if a NUC process has not previously been performed then the updated NUCterms may be new NUC terms in the first iteration of block 570).

For example, FIG. 7 illustrates a flat field correction technique 700 inaccordance with an embodiment of the disclosure. In FIG. 7, a NUC termmay be determined for each pixel 710 of the blurred image frame usingthe values of its neighboring pixels 712 to 726. For each pixel 710,several gradients may be determined based on the absolute differencebetween the values of various adjacent pixels. For example, absolutevalue differences may be determined between: pixels 712 and 714 (a leftto right diagonal gradient), pixels 716 and 718 (a top to bottomvertical gradient), pixels 720 and 722 (a right to left diagonalgradient), and pixels 724 and 726 (a left to right horizontal gradient).

These absolute differences may be summed to provide a summed gradientfor pixel 710. A weight value may be determined for pixel 710 that isinversely proportional to the summed gradient. This process may beperformed for all pixels 710 of the blurred image frame until a weightvalue is provided for each pixel 710. For areas with low gradients(e.g., areas that are blurry or have low contrast), the weight valuewill be close to one. Conversely, for areas with high gradients, theweight value will be zero or close to zero. The update to the NUC termas estimated by the high pass filter is multiplied with the weightvalue.

In one embodiment, the risk of introducing scene information into theNUC terms can be further reduced by applying some amount of temporaldamping to the NUC term determination process. For example, a temporaldamping factor λ between 0 and 1 may be chosen such that the new NUCterm (NUC_(NEW)) stored is a weighted average of the old NUC term(NUC_(OLD)) and the estimated updated NUC term (NUC_(UPDATE)). In oneembodiment, this can be expressed asNUC_(NEW)=λ·NUC_(OLD)+(1−λ)·(NUC_(OLD)+NUC_(UPDATE)).

Although the determination of NUC terms has been described with regardto gradients, local contrast values may be used instead whereappropriate. Other techniques may also be used such as, for example,standard deviation calculations. Other types flat field correctionprocesses may be performed to determine NUC terms including, forexample, various processes identified in U.S. Pat. No. 6,028,309 issuedFeb. 22, 2000, U.S. Pat. No. 6,812,465 issued Nov. 2, 2004, and U.S.patent application Ser. No. 12/114,865 filed May 5, 2008, which areincorporated herein by reference in their entirety.

Referring again to FIG. 5, block 570 may include additional processingof the NUC terms. For example, in one embodiment, to preserve the scenesignal mean, the sum of all NUC terms may be normalized to zero bysubtracting the NUC term mean from each NUC term. Also in block 570, toavoid row and column noise from affecting the NUC terms, the mean valueof each row and column may be subtracted from the NUC terms for each rowand column. As a result, row and column FPN filters using the row andcolumn FPN terms determined in block 550 may be better able to filterout row and column noise in further iterations (e.g., as further shownin FIG. 8) after the NUC terms are applied to captured images (e.g., inblock 580 further discussed herein). In this regard, the row and columnFPN filters may in general use more data to calculate the per row andper column offset coefficients (e.g., row and column FPN terms) and maythus provide a more robust alternative for reducing spatially correlatedFPN than the NUC terms which are based on high pass filtering to capturespatially uncorrelated noise.

In blocks 571-573, additional high pass filtering and furtherdeterminations of updated NUC terms may be optionally performed toremove spatially correlated FPN with lower spatial frequency thanpreviously removed by row and column FPN terms. In this regard, somevariability in infrared sensors 132 or other components of infraredimaging module 100 may result in spatially correlated FPN noise thatcannot be easily modeled as row or column noise. Such spatiallycorrelated FPN may include, for example, window defects on a sensorpackage or a cluster of infrared sensors 132 that respond differently toirradiance than neighboring infrared sensors 132. In one embodiment,such spatially correlated FPN may be mitigated with an offsetcorrection. If the amount of such spatially correlated FPN issignificant, then the noise may also be detectable in the blurred imageframe. Since this type of noise may affect a neighborhood of pixels, ahigh pass filter with a small kernel may not detect the FPN in theneighborhood (e.g., all values used in high pass filter may be takenfrom the neighborhood of affected pixels and thus may be affected by thesame offset error). For example, if the high pass filtering of block 565is performed with a small kernel (e.g., considering only immediatelyadjacent pixels that fall within a neighborhood of pixels affected byspatially correlated FPN), then broadly distributed spatially correlatedFPN may not be detected.

For example, FIG. 11 illustrates spatially correlated FPN in aneighborhood of pixels in accordance with an embodiment of thedisclosure. As shown in a sample image frame 1100, a neighborhood ofpixels 1110 may exhibit spatially correlated FPN that is not preciselycorrelated to individual rows and columns and is distributed over aneighborhood of several pixels (e.g., a neighborhood of approximately 4by 4 pixels in this example). Sample image frame 1100 also includes aset of pixels 1120 exhibiting substantially uniform response that arenot used in filtering calculations, and a set of pixels 1130 that areused to estimate a low pass value for the neighborhood of pixels 1110.In one embodiment, pixels 1130 may be a number of pixels divisible bytwo in order to facilitate efficient hardware or software calculations.

Referring again to FIG. 5, in blocks 571-573, additional high passfiltering and further determinations of updated NUC terms may beoptionally performed to remove spatially correlated FPN such asexhibited by pixels 1110. In block 571, the updated NUC terms determinedin block 570 are applied to the blurred image frame. Thus, at this time,the blurred image frame will have been initially corrected for spatiallycorrelated FPN (e.g., by application of the updated row and column FPNterms in block 555), and also initially corrected for spatiallyuncorrelated FPN (e.g., by application of the updated NUC terms appliedin block 571).

In block 572, a further high pass filter is applied with a larger kernelthan was used in block 565, and further updated NUC terms may bedetermined in block 573. For example, to detect the spatially correlatedFPN present in pixels 1110, the high pass filter applied in block 572may include data from a sufficiently large enough neighborhood of pixelssuch that differences can be determined between unaffected pixels (e.g.,pixels 1120) and affected pixels (e.g., pixels 1110). For example, a lowpass filter with a large kernel can be used (e.g., an N by N kernel thatis much greater than 3 by 3 pixels) and the results may be subtracted toperform appropriate high pass filtering.

In one embodiment, for computational efficiency, a sparse kernel may beused such that only a small number of neighboring pixels inside an N byN neighborhood are used. For any given high pass filter operation usingdistant neighbors (e.g., a large kernel), there is a risk of modelingactual (potentially blurred) scene information as spatially correlatedFPN. Accordingly, in one embodiment, the temporal damping factor λ maybe set close to 1 for updated NUC terms determined in block 573.

In various embodiments, blocks 571-573 may be repeated (e.g., cascaded)to iteratively perform high pass filtering with increasing kernel sizesto provide further updated NUC terms further correct for spatiallycorrelated FPN of desired neighborhood sizes. In one embodiment, thedecision to perform such iterations may be determined by whetherspatially correlated FPN has actually been removed by the updated NUCterms of the previous performance of blocks 571-573.

After blocks 571-573 are finished, a decision is made regarding whetherto apply the updated NUC terms to captured image frames (block 574). Forexample, if an average of the absolute value of the NUC terms for theentire image frame is less than a minimum threshold value, or greaterthan a maximum threshold value, the NUC terms may be deemed spurious orunlikely to provide meaningful correction. Alternatively, thresholdingcriteria may be applied to individual pixels to determine which pixelsreceive updated NUC terms. In one embodiment, the threshold values maycorrespond to differences between the newly calculated NUC terms andpreviously calculated NUC terms. In another embodiment, the thresholdvalues may be independent of previously calculated NUC terms. Othertests may be applied (e.g., spatial correlation tests) to determinewhether the NUC terms should be applied.

If the NUC terms are deemed spurious or unlikely to provide meaningfulcorrection, then the flow diagram returns to block 505. Otherwise, thenewly determined NUC terms are stored (block 575) to replace previousNUC terms (e.g., determined by a previously performed iteration of FIG.5) and applied (block 580) to captured image frames.

FIG. 8 illustrates various image processing techniques of FIG. 5 andother operations applied in an image processing pipeline 800 inaccordance with an embodiment of the disclosure. In this regard,pipeline 800 identifies various operations of FIG. 5 in the context ofan overall iterative image processing scheme for correcting image framesprovided by infrared imaging module 100. In some embodiments, pipeline800 may be provided by processing module 160 or processor 195 (both alsogenerally referred to as a processor) operating on image frames capturedby infrared sensors 132.

Image frames captured by infrared sensors 132 may be provided to a frameaverager 804 that integrates multiple image frames to provide imageframes 802 with an improved signal to noise ratio. Frame averager 804may be effectively provided by infrared sensors 132, ROIC 402, and othercomponents of infrared sensor assembly 128 that are implemented tosupport high image capture rates. For example, in one embodiment,infrared sensor assembly 128 may capture infrared image frames at aframe rate of 240 Hz (e.g., 240 images per second). In this embodiment,such a high frame rate may be implemented, for example, by operatinginfrared sensor assembly 128 at relatively low voltages (e.g.,compatible with mobile telephone voltages) and by using a relativelysmall array of infrared sensors 132 (e.g., an array of 64 by 64 infraredsensors in one embodiment).

In one embodiment, such infrared image frames may be provided frominfrared sensor assembly 128 to processing module 160 at a high framerate (e.g., 240 Hz or other frame rates). In another embodiment,infrared sensor assembly 128 may integrate over longer time periods, ormultiple time periods, to provide integrated (e.g., averaged) infraredimage frames to processing module 160 at a lower frame rate (e.g., 30Hz, 9 Hz, or other frame rates). Further information regardingimplementations that may be used to provide high image capture rates maybe found in U.S. Provisional Patent Application No. 61/495,879 filedJun. 10, 2011 which is incorporated herein by reference in its entirety.

Image frames 802 proceed through pipeline 800 where they are adjusted byvarious terms, temporally filtered, used to determine the variousadjustment terms, and gain compensated.

In blocks 810 and 814, factory gain terms 812 and factory offset terms816 are applied to image frames 802 to compensate for gain and offsetdifferences, respectively, between the various infrared sensors 132and/or other components of infrared imaging module 100 determined duringmanufacturing and testing.

In block 580, NUC terms 817 are applied to image frames 802 to correctfor FPN as discussed. In one embodiment, if NUC terms 817 have not yetbeen determined (e.g., before a NUC process has been initiated), thenblock 580 may not be performed or initialization values may be used forNUC terms 817 that result in no alteration to the image data (e.g.,offsets for every pixel would be equal to zero).

In blocks 818 and 822, column FPN terms 820 and row FPN terms 824,respectively, are applied to image frames 802. Column FPN terms 820 androw FPN terms 824 may be determined in accordance with block 550 asdiscussed. In one embodiment, if the column FPN terms 820 and row FPNterms 824 have not yet been determined (e.g., before a NUC process hasbeen initiated), then blocks 818 and 822 may not be performed orinitialization values may be used for the column FPN terms 820 and rowFPN terms 824 that result in no alteration to the image data (e.g.,offsets for every pixel would be equal to zero).

In block 826, temporal filtering is performed on image frames 802 inaccordance with a temporal noise reduction (TNR) process. FIG. 9illustrates a TNR process in accordance with an embodiment of thedisclosure. In FIG. 9, a presently received image frame 802 a and apreviously temporally filtered image frame 802 b are processed todetermine a new temporally filtered image frame 802 e. Image frames 802a and 802 b include local neighborhoods of pixels 803 a and 803 bcentered around pixels 805 a and 805 b, respectively. Neighborhoods 803a and 803 b correspond to the same locations within image frames 802 aand 802 b and are subsets of the total pixels in image frames 802 a and802 b. In the illustrated embodiment, neighborhoods 803 a and 803 binclude areas of 5 by 5 pixels. Other neighborhood sizes may be used inother embodiments.

Differences between corresponding pixels of neighborhoods 803 a and 803b are determined and averaged to provide an averaged delta value 805 cfor the location corresponding to pixels 805 a and 805 b. Averaged deltavalue 805 c may be used to determine weight values in block 807 to beapplied to pixels 805 a and 805 b of image frames 802 a and 802 b.

In one embodiment, as shown in graph 809, the weight values determinedin block 807 may be inversely proportional to averaged delta value 805 csuch that weight values drop rapidly towards zero when there are largedifferences between neighborhoods 803 a and 803 b. In this regard, largedifferences between neighborhoods 803 a and 803 b may indicate thatchanges have occurred within the scene (e.g., due to motion) and pixels802 a and 802 b may be appropriately weighted, in one embodiment, toavoid introducing blur across frame-to-frame scene changes. Otherassociations between weight values and averaged delta value 805 c may beused in various embodiments.

The weight values determined in block 807 may be applied to pixels 805 aand 805 b to determine a value for corresponding pixel 805 e of imageframe 802 e (block 811). In this regard, pixel 805 e may have a valuethat is a weighted average (or other combination) of pixels 805 a and805 b, depending on averaged delta value 805 c and the weight valuesdetermined in block 807.

For example, pixel 805 e of temporally filtered image frame 802 e may bea weighted sum of pixels 805 a and 805 b of image frames 802 a and 802b. If the average difference between pixels 805 a and 805 b is due tonoise, then it may be expected that the average change betweenneighborhoods 805 a and 805 b will be close to zero (e.g., correspondingto the average of uncorrelated changes). Under such circumstances, itmay be expected that the sum of the differences between neighborhoods805 a and 805 b will be close to zero. In this case, pixel 805 a ofimage frame 802 a may both be appropriately weighted so as to contributeto the value of pixel 805 e.

However, if the sum of such differences is not zero (e.g., evendiffering from zero by a small amount in one embodiment), then thechanges may be interpreted as being attributed to motion instead ofnoise. Thus, motion may be detected based on the average changeexhibited by neighborhoods 805 a and 805 b. Under these circumstances,pixel 805 a of image frame 802 a may be weighted heavily, while pixel805 b of image frame 802 b may be weighted lightly.

Other embodiments are also contemplated. For example, although averageddelta value 805 c has been described as being determined based onneighborhoods 805 a and 805 b, in other embodiments averaged delta value805 c may be determined based on any desired criteria (e.g., based onindividual pixels or other types of groups of sets of pixels).

In the above embodiments, image frame 802 a has been described as apresently received image frame and image frame 802 b has been describedas a previously temporally filtered image frame. In another embodiment,image frames 802 a and 802 b may be first and second image framescaptured by infrared imaging module 100 that have not been temporallyfiltered.

FIG. 10 illustrates further implementation details in relation to theTNR process of block 826. As shown in FIG. 10, image frames 802 a and802 b may be read into line buffers 1010 a and 1010 b, respectively, andimage frame 802 b (e.g., the previous image frame) may be stored in aframe buffer 1020 before being read into line buffer 1010 b. In oneembodiment, line buffers 1010 a-b and frame buffer 1020 may beimplemented by a block of random access memory (RAM) provided by anyappropriate component of infrared imaging module 100 and/or host device102.

Referring again to FIG. 8, image frame 802 e may be passed to anautomatic gain compensation block 828 for further processing to providea result image frame 830 that may be used by host device 102 as desired.

FIG. 8 further illustrates various operations that may be performed todetermine row and column FPN terms and NUC terms as discussed. In oneembodiment, these operations may use image frames 802 e as shown in FIG.8. Because image frames 802 e have already been temporally filtered, atleast some temporal noise may be removed and thus will not inadvertentlyaffect the determination of row and column FPN terms 824 and 820 and NUCterms 817. In another embodiment, non-temporally filtered image frames802 may be used.

In FIG. 8, blocks 510, 515, and 520 of FIG. 5 are collectivelyrepresented together. As discussed, a NUC process may be selectivelyinitiated and performed in response to various NUC process initiatingevents and based on various criteria or conditions. As also discussed,the NUC process may be performed in accordance with a motion-basedapproach (blocks 525, 535, and 540) or a defocus-based approach (block530) to provide a blurred image frame (block 545). FIG. 8 furtherillustrates various additional blocks 550, 552, 555, 560, 565, 570, 571,572, 573, and 575 previously discussed with regard to FIG. 5.

As shown in FIG. 8, row and column FPN terms 824 and 820 and NUC terms817 may be determined and applied in an iterative fashion such thatupdated terms are determined using image frames 802 to which previousterms have already been applied. As a result, the overall process ofFIG. 8 may repeatedly update and apply such terms to continuously reducethe noise in image frames 830 to be used by host device 102.

Referring again to FIG. 10, further implementation details areillustrated for various blocks of FIGS. 5 and 8 in relation to pipeline800. For example, blocks 525, 535, and 540 are shown as operating at thenormal frame rate of image frames 802 received by pipeline 800. In theembodiment shown in FIG. 10, the determination made in block 525 isrepresented as a decision diamond used to determine whether a givenimage frame 802 has sufficiently changed such that it may be consideredan image frame that will enhance the blur if added to other image framesand is therefore accumulated (block 535 is represented by an arrow inthis embodiment) and averaged (block 540).

Also in FIG. 10, the determination of column FPN terms 820 (block 550)is shown as operating at an update rate that in this example is 1/32 ofthe sensor frame rate (e.g., normal frame rate) due to the averagingperformed in block 540. Other update rates may be used in otherembodiments. Although only column FPN terms 820 are identified in FIG.10, row FPN terms 824 may be implemented in a similar fashion at thereduced frame rate.

FIG. 10 also illustrates further implementation details in relation tothe NUC determination process of block 570. In this regard, the blurredimage frame may be read to a line buffer 1030 (e.g., implemented by ablock of RAM provided by any appropriate component of infrared imagingmodule 100 and/or host device 102). The flat field correction technique700 of FIG. 7 may be performed on the blurred image frame.

In view of the present disclosure, it will be appreciated thattechniques described herein may be used to remove various types of FPN(e.g., including very high amplitude FPN) such as spatially correlatedrow and column FPN and spatially uncorrelated FPN.

Other embodiments are also contemplated. For example, in one embodiment,the rate at which row and column FPN terms and/or NUC terms are updatedcan be inversely proportional to the estimated amount of blur in theblurred image frame and/or inversely proportional to the magnitude oflocal contrast values (e.g., determined in block 560).

In various embodiments, the described techniques may provide advantagesover conventional shutter-based noise correction techniques. Forexample, by using a shutterless process, a shutter (e.g., such asshutter 105) need not be provided, thus permitting reductions in size,weight, cost, and mechanical complexity. Power and maximum voltagesupplied to, or generated by, infrared imaging module 100 may also bereduced if a shutter does not need to be mechanically operated.Reliability will be improved by removing the shutter as a potentialpoint of failure. A shutterless process also eliminates potential imageinterruption caused by the temporary blockage of the imaged scene by ashutter.

Also, by correcting for noise using intentionally blurred image framescaptured from a real world scene (not a uniform scene provided by ashutter), noise correction may be performed on image frames that haveirradiance levels similar to those of the actual scene desired to beimaged. This can improve the accuracy and effectiveness of noisecorrection terms determined in accordance with the various describedtechniques.

As discussed, in various embodiments, infrared imaging module 100 may beconfigured to operate at low voltage levels. In particular, infraredimaging module 100 may be implemented with circuitry configured tooperate at low power and/or in accordance with other parameters thatpermit infrared imaging module 100 to be conveniently and effectivelyimplemented in various types of host devices 102, such as mobile devicesand other devices.

For example, FIG. 12 illustrates a block diagram of anotherimplementation of infrared sensor assembly 128 including infraredsensors 132 and an LDO 1220 in accordance with an embodiment of thedisclosure. As shown, FIG. 12 also illustrates various components 1202,1204, 1205, 1206, 1208, and 1210 which may implemented in the same orsimilar manner as corresponding components previously described withregard to FIG. 4, FIG. 12 also illustrates bias correction circuitry1212 which may be used to adjust one or more bias voltages provided toinfrared sensors 132 (e.g., to compensate for temperature changes,self-heating, and/or other factors).

In some embodiments, LDO 1220 may be provided as part of infrared sensorassembly 128 (e.g., on the same chip and/or wafer level package as theROIC). For example, LDO 1220 may be provided as part of an FPA withinfrared sensor assembly 128. As discussed, such implementations mayreduce power supply noise introduced to infrared sensor assembly 128 andthus provide an improved PSRR. In addition, by implementing the LDO withthe ROIC, less die area may be consumed and fewer discrete die (orchips) are needed.

LDO 1220 receives an input voltage provided by a power source 1230 overa supply line 1232. LDO 1220 provides an output voltage to variouscomponents of infrared sensor assembly 128 over supply lines 1222. Inthis regard, LDO 1220 may provide substantially identical regulatedoutput voltages to various components of infrared sensor assembly 128 inresponse to a single input voltage received from power source 1230, inaccordance with various techniques described in, for example, U.S.patent application Ser. No. 14/101,245 filed Dec. 9, 2013 incorporatedherein by reference in its entirety.

For example, in some embodiments, power source 1230 may provide an inputvoltage in a range of approximately 2.8 volts to approximately 11 volts(e.g., approximately 2.8 volts in one embodiment), and LDO 1220 mayprovide an output voltage in a range of approximately 1.5 volts toapproximately 2.8 volts (e.g., approximately 2.8, 2.5, 2.4, and/or lowervoltages in various embodiments). In this regard, LDO 1220 may be usedto provide a consistent regulated output voltage, regardless of whetherpower source 1230 is implemented with a conventional voltage range ofapproximately 9 volts to approximately 11 volts, or a low voltage suchas approximately 2.8 volts. As such, although various voltage ranges areprovided for the input and output voltages, it is contemplated that theoutput voltage of LDO 1220 will remain fixed despite changes in theinput voltage.

The implementation of LDO 1220 as part of infrared sensor assembly 128provides various advantages over conventional power implementations forFPAs. For example, conventional FPAs typically rely on multiple powersources, each of which may be provided separately to the FPA, andseparately distributed to the various components of the FPA. Byregulating a single power source 1230 by LDO 1220, appropriate voltagesmay be separately provided (e.g., to reduce possible noise) to allcomponents of infrared sensor assembly 128 with reduced complexity. Theuse of LDO 1220 also allows infrared sensor assembly 128 to operate in aconsistent manner, even if the input voltage from power source 1230changes (e.g., if the input voltage increases or decreases as a resultof charging or discharging a battery or other type of device used forpower source 1230).

The various components of infrared sensor assembly 128 shown in FIG. 12may also be implemented to operate at lower voltages than conventionaldevices. For example, as discussed, LDO 1220 may be implemented toprovide a low voltage (e.g., approximately 2.5 volts). This contrastswith the multiple higher voltages typically used to power conventionalFPAs, such as: approximately 3.3 volts to approximately 5 volts used topower digital circuitry; approximately 3.3 volts used to power analogcircuitry; and approximately 9 volts to approximately 11 volts used topower loads. Also, in some embodiments, the use of LDO 1220 may reduceor eliminate the need for a separate negative reference voltage to beprovided to infrared sensor assembly 128.

Additional aspects of the low voltage operation of infrared sensorassembly 128 may be further understood with reference to FIG. 13. FIG.13 illustrates a circuit diagram of a portion of infrared sensorassembly 128 of FIG. 12 in accordance with an embodiment of thedisclosure. In particular, FIG. 13 illustrates additional components ofbias correction circuitry 1212 (e.g., components 1326, 1330, 1332, 1334,1336, 1338, and 1341) connected to LDO 1220 and infrared sensors 132.For example, bias correction circuitry 1212 may be used to compensatefor temperature-dependent changes in bias voltages in accordance with anembodiment of the present disclosure. The operation of such additionalcomponents may be further understood with reference to similarcomponents identified in U.S. Pat. No. 7,679,048 issued Mar. 16, 2010which is hereby incorporated by reference in its entirety. Infraredsensor assembly 128 may also be implemented in accordance with thevarious components identified in U.S. Pat. No. 6,812,465 issued Nov. 2,2004 which is hereby incorporated by reference in its entirety.

In various embodiments, some or all of the bias correction circuitry1212 may be implemented on a global array basis as shown in FIG. 13(e.g., used for all infrared sensors 132 collectively in an array). Inother embodiments, some or all of the bias correction circuitry 1212 maybe implemented an individual sensor basis (e.g., entirely or partiallyduplicated for each infrared sensor 132). In some embodiments, biascorrection circuitry 1212 and other components of FIG. 13 may beimplemented as part of ROIC 1202.

As shown in FIG. 13, LDO 1220 provides a load voltage Vload to biascorrection circuitry 1212 along one of supply lines 1222. As discussed,in some embodiments, Vload may be approximately 2.5 volts whichcontrasts with larger voltages of approximately 9 volts to approximately11 volts that may be used as load voltages in conventional infraredimaging devices.

Based on Vload, bias correction circuitry 1212 provides a sensor biasvoltage Vbolo at a node 1360. Vbolo may be distributed to one or moreinfrared sensors 132 through appropriate switching circuitry 1370 (e.g.,represented by broken lines in FIG. 13). In some examples, switchingcircuitry 1370 may be implemented in accordance with appropriatecomponents identified in U.S. Pat. Nos. 6,812,465 and 7,679,048previously referenced herein.

Each infrared sensor 132 includes a node 1350 which receives Vbolothrough switching circuitry 1370, and another node 1352 which may beconnected to ground, a substrate, and/or a negative reference voltage.In some embodiments, the voltage at node 1360 may be substantially thesame as Vbolo provided at nodes 1350. In other embodiments, the voltageat node 1360 may be adjusted to compensate for possible voltage dropsassociated with switching circuitry 1370 and/or other factors.

Vbolo may be implemented with lower voltages than are typically used forconventional infrared sensor biasing. In one embodiment, Vbolo may be ina range of approximately 0.2 volts to approximately 0.7 volts. Inanother embodiment, Vbolo may be in a range of approximately 0.4 voltsto approximately 0.6 volts. In another embodiment, Vbolo may beapproximately 0.5 volts. In contrast, conventional infrared sensorstypically use bias voltages of approximately 1 volt.

The use of a lower bias voltage for infrared sensors 132 in accordancewith the present disclosure permits infrared sensor assembly 128 toexhibit significantly reduced power consumption in comparison withconventional infrared imaging devices. In particular, the powerconsumption of each infrared sensor 132 is reduced by the square of thebias voltage. As a result, a reduction from, for example, 1.0 volt to0.5 volts provides a significant reduction in power, especially whenapplied to many infrared sensors 132 in an infrared sensor array. Thisreduction in power may also result in reduced self-heating of infraredsensor assembly 128.

In accordance with additional embodiments of the present disclosure,various techniques are provided for reducing the effects of noise inimage frames provided by infrared imaging devices operating at lowvoltages. In this regard, when infrared sensor assembly 128 is operatedwith low voltages as described, noise, self-heating, and/or otherphenomena may, if uncorrected, become more pronounced in image framesprovided by infrared sensor assembly 128.

For example, referring to FIG. 13, when LDO 1220 maintains Vload at alow voltage in the manner described herein, Vbolo will also bemaintained at its corresponding low voltage and the relative size of itsoutput signals may be reduced. As a result, noise, self-heating, and/orother phenomena may have a greater effect on the smaller output signalsread out from infrared sensors 132, resulting in variations (e.g.,errors) in the output signals. If uncorrected, these variations may beexhibited as noise in the image frames. Moreover, although low voltageoperation may reduce the overall amount of certain phenomena (e.g.,self-heating), the smaller output signals may permit the remaining errorsources (e.g., residual self-heating) to have a disproportionate effecton the output signals during low voltage operation.

To compensate for such phenomena, infrared sensor assembly 128, infraredimaging module 100, and/or host device 102 may be implemented withvarious array sizes, frame rates, and/or frame averaging techniques. Forexample, as discussed, a variety of different array sizes arecontemplated for infrared sensors 132. In some embodiments, infraredsensors 132 may be implemented with array sizes ranging from 32 by 32 to160 by 120 infrared sensors 132. Other example array sizes include 80 by64, 80 by 60, 64 by 64, and 64 by 32. Any desired array size may beused.

Advantageously, when implemented with such relatively small array sizes,infrared sensor assembly 128 may provide image frames at relatively highframe rates without requiring significant changes to ROIC and relatedcircuitry. For example, in some embodiments, frame rates may range fromapproximately 120 Hz to approximately 480 Hz.

In some embodiments, the array size and the frame rate may be scaledrelative to each other (e.g., in an inversely proportional manner orotherwise) such that larger arrays are implemented with lower framerates, and smaller arrays are implemented with higher frame rates. Forexample, in one embodiment, an array of 160 by 120 may provide a framerate of approximately 120 Hz. In another embodiment, an array of 80 by60 may provide a correspondingly higher frame rate of approximately 240Hz. Other frame rates are also contemplated.

By scaling the array size and the frame rate relative to each other, theparticular readout timing of rows and/or columns of the FPA may remainconsistent, regardless of the actual FPA size or frame rate. In oneembodiment, the readout timing may be approximately 63 microseconds perrow or column.

As previously discussed with regard to FIG. 8, the image frames capturedby infrared sensors 132 may be provided to a frame averager 804 thatintegrates multiple image frames to provide image frames 802 (e.g.,processed image frames) with a lower frame rate (e.g., approximately 30Hz, approximately 60 Hz, or other frame rates) and with an improvedsignal to noise ratio. In particular, by averaging the high frame rateimage frames provided by a relatively small FPA, image noiseattributable to low voltage operation may be effectively averaged outand/or substantially reduced in image frames 802. Accordingly, infraredsensor assembly 128 may be operated at relatively low voltages providedby LDO 1220 as discussed without experiencing additional noise andrelated side effects in the resulting image frames 802 after processingby frame averager 804.

Other embodiments are also contemplated. For example, although a singlearray of infrared sensors 132 is illustrated, it is contemplated thatmultiple such arrays may be used together to provide higher resolutionimage frames (e.g., a scene may be imaged across multiple such arrays).Such arrays may be provided in multiple infrared sensor assemblies 128and/or provided in the same infrared sensor assembly 128. Each sucharray may be operated at low voltages as described, and also may beprovided with associated ROIC circuitry such that each array may stillbe operated at a relatively high frame rate. The high frame rate imageframes provided by such arrays may be averaged by shared or dedicatedframe averagers 804 to reduce and/or eliminate noise associated with lowvoltage operation. As a result, high resolution infrared images may beobtained while still operating at low voltages.

In various embodiments, infrared sensor assembly 128 may be implementedwith appropriate dimensions to permit infrared imaging module 100 to beused with a small form factor socket 104, such as a socket used formobile devices. For example, in some embodiments, infrared sensorassembly 128 may be implemented with a chip size in a range ofapproximately 4.0 mm by approximately 4.0 mm to approximately 5.5 mm byapproximately 5.5 mm (e.g., approximately 4.0 mm by approximately 5.5 mmin one example). Infrared sensor assembly 128 may be implemented withsuch sizes or other appropriate sizes to permit use with socket 104implemented with various sizes such as: 8.5 mm by 8.5 mm, 8.5 mm by 5.9mm, 6.0 mm by 6.0 mm, 5.5 mm by 5.5 mm, 4.5 mm by 4.5 mm, and/or othersocket sizes such as, for example, those identified in Table 1 of U.S.Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011incorporated herein by reference in its entirety.

As further described with regard to FIGS. 14-23E, various imageprocessing techniques are described which may be applied, for example,to infrared images (e.g., thermal images) to reduce noise within theinfrared images (e.g., improve image detail and/or image quality) and/orprovide non-uniformity correction.

Although FIGS. 14-23E will be primarily described with regard to asystem 2100, the described techniques may be performed by processingmodule 160 or processor 195 (both also generally referred to as aprocessor) operating on image frames captured by infrared sensors 132,and vice versa.

In some embodiments, the techniques described with regard to FIGS.14-22B be used to perform operations of block 550 (see FIGS. 5 and 8) todetermine row and/or column FPN terms. For example, such techniques maybe applied to intentionally blurred images provided by block 545 ofFIGS. 5 and 8. In some embodiments, the techniques described with regardto FIGS. 23A-E may be used in place of and/or in addition to theoperations of blocks 565-573 (see FIGS. 5 and 8) to estimate FPN and/ordetermine NUC terms.

Referring now to FIGS. 14-22B, a significant portion of noise may bedefined as row and column noise. This type of noise may be explained bynon-linearities in a Read Out Integrated Circuit (ROIC). This type ofnoise, if not eliminated, may manifest as vertical and horizontalstripes in the final image and human observers are particularlysensitive to these types of image artifacts. Other systems relying onimagery from infrared sensors, such as, for example, automatic targettrackers may also suffer from performance degradation, if row and columnnoise is present.

Because of non-linear behavior of infrared detectors and read-outintegrated circuit (ROIC) assemblies, even when a shutter operation orexternal black body calibration is performed, there may be residual rowand column noise (e.g., the scene being imaged may not have the exactsame temperature as the shutter). The amount of row and column noise mayincrease over time, after offset calibration, increasing asymptoticallyto some maximum value. In one aspect, this may be referred to as 1/ftype noise.

In any given frame, the row and column noise may be viewed as highfrequency spatial noise. Conventionally, this type of noise may bereduced using filters in the spatial domain (e.g., local linear ornon-linear low pass filters) or the frequency domain (e.g., low passfilters in Fourier or Wavelet space). However, these filters may havenegative side effects, such as blurring of the image and potential lossof faint details.

It should be appreciated by those skilled in the art that any referenceto a column or a row may include a partial column or a partial row andthat the terms “row” and “column” are interchangeable and not limiting.Thus, without departing from the scope of the invention, the term “row”may be used to describe a row or a column, and likewise, the term“column” may be used to describe a row or a column, depending upon theapplication.

FIG. 14 shows a block diagram of system 2100 (e.g., an infrared camera)for infrared image capturing and processing in accordance with anembodiment. In some embodiments, system 2100 may be implemented byinfrared imaging module 100, host device 102, infrared sensor assembly128, and/or various components described herein (e.g., see FIGS. 1-13).Accordingly, although various techniques are described with regard tosystem 2100, such techniques may be similarly applied to infraredimaging module 100, host device 102, infrared sensor assembly 128,and/or various components described herein, and vice versa.

The system 2100 comprises, in one implementation, a processing component2110, a memory component 2120, an image capture component 2130, acontrol component 2140, and a display component 2150. Optionally, thesystem 2100 may include a sensing component 2160.

The system 2100 may represent an infrared imaging device, such as aninfrared camera, to capture and process images, such as video images ofa scene 2170. The system 2100 may represent any type of infrared cameraadapted to detect infrared radiation and provide representative data andinformation (e.g., infrared image data of a scene). For example, thesystem 2100 may represent an infrared camera that is directed to thenear, middle, and/or far infrared spectrums. In another example, theinfrared image data may comprise non-uniform data (e.g., real image datathat is not from a shutter or black body) of the scene 2170, forprocessing, as set forth herein. The system 2100 may comprise a portabledevice and may be incorporated, e.g., into a vehicle (e.g., anautomobile or other type of land-based vehicle, an aircraft, or aspacecraft) or a non-mobile installation requiring infrared images to bestored and/or displayed.

In various embodiments, the processing component 2110 comprises aprocessor, such as one or more of a microprocessor, a single-coreprocessor, a multi-core processor, a microcontroller, a logic device(e.g., a programmable logic device (PLD) configured to performprocessing functions), a digital signal processing (DSP) device, etc.The processing component 2110 may be adapted to interface andcommunicate with components 2120, 2130, 2140, and 2150 to perform methodand processing steps and/or operations, as described herein. Theprocessing component 2110 may include a noise filtering module 2112adapted to implement a noise reduction and/or removal algorithm (e.g., anoise filtering algorithm, such as any of those discussed herein). Inone aspect, the processing component 2110 may be adapted to performvarious other image processing algorithms including scaling the infraredimage data, either as part of or separate from the noise filteringalgorithm.

It should be appreciated that noise filtering module 2112 may beintegrated in software and/or hardware as part of the processingcomponent 2110, with code (e.g., software or configuration data) for thenoise filtering module 2112 stored, e.g., in the memory component 2120.Embodiments of the noise filtering algorithm, as disclosed herein, maybe stored by a separate computer-readable medium (e.g., a memory, suchas a hard drive, a compact disk, a digital video disk, or a flashmemory) to be executed by a computer (e.g., a logic or processor-basedsystem) to perform various methods and operations disclosed herein. Inone aspect, the computer-readable medium may be portable and/or locatedseparate from the system 2100, with the stored noise filtering algorithmprovided to the system 2100 by coupling the computer-readable medium tothe system 2100 and/or by the system 2100 downloading (e.g., via a wiredlink and/or a wireless link) the noise filtering algorithm from thecomputer-readable medium.

The memory component 2120 comprises, in one embodiment, one or morememory devices adapted to store data and information, including infrareddata and information. The memory device 2120 may comprise one or morevarious types of memory devices including volatile and non-volatilememory devices, such as RAM (Random Access Memory), ROM (Read-OnlyMemory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory,etc. The processing component 2110 may be adapted to execute softwarestored in the memory component 2120 so as to perform method and processsteps and/or operations described herein.

The image capture component 2130 comprises, in one embodiment, one ormore infrared sensors (e.g., any type of multi-pixel infrared detector,such as a focal plane array) for capturing infrared image data (e.g.,still image data and/or video data) representative of an image, such asscene 2170. In one implementation, the infrared sensors of the imagecapture component 2130 provide for representing (e.g., converting) thecaptured image data as digital data (e.g., via an analog-to-digitalconverter included as part of the infrared sensor or separate from theinfrared sensor as part of the system 2100). In one aspect, the infraredimage data (e.g., infrared video data) may comprise non-uniform data(e.g., real image data) of an image, such as scene 2170. The processingcomponent 2110 may be adapted to process the infrared image data (e.g.,to provide processed image data), store the infrared image data in thememory component 2120, and/or retrieve stored infrared image data fromthe memory component 2120. For example, the processing component 2110may be adapted to process infrared image data stored in the memorycomponent 2120 to provide processed image data and information (e.g.,captured and/or processed infrared image data).

The control component 2140 comprises, in one embodiment, a user inputand/or interface device, such as a rotatable knob (e.g., potentiometer),push buttons, slide bar, keyboard, etc., that is adapted to generate auser input control signal. The processing component 2110 may be adaptedto sense control input signals from a user via the control component2140 and respond to any sensed control input signals received therefrom.The processing component 2110 may be adapted to interpret such a controlinput signal as a value, as generally understood by one skilled in theart.

In one embodiment, the control component 2140 may comprise a controlunit (e.g., a wired or wireless handheld control unit) having pushbuttons adapted to interface with a user and receive user input controlvalues. In one implementation, the push buttons of the control unit maybe used to control various functions of the system 2100, such asautofocus, menu enable and selection, field of view, brightness,contrast, noise filtering, high pass filtering, low pass filtering,and/or various other features as understood by one skilled in the art.In another implementation, one or more of the push buttons may be usedto provide input values (e.g., one or more noise filter values,adjustment parameters, characteristics, etc.) for a noise filteralgorithm. For example, one or more push buttons may be used to adjustnoise filtering characteristics of infrared images captured and/orprocessed by the system 2100.

The display component 2150 comprises, in one embodiment, an imagedisplay device (e.g., a liquid crystal display (LCD)) or various othertypes of generally known video displays or monitors. The processingcomponent 2110 may be adapted to display image data and information onthe display component 2150. The processing component 2110 may be adaptedto retrieve image data and information from the memory component 2120and display any retrieved image data and information on the displaycomponent 2150. The display component 2150 may comprise displayelectronics, which may be utilized by the processing component 2110 todisplay image data and information (e.g., infrared images). The displaycomponent 2150 may be adapted to receive image data and informationdirectly from the image capture component 2130 via the processingcomponent 2110, or the image data and information may be transferredfrom the memory component 2120 via the processing component 2110.

The optional sensing component 2160 comprises, in one embodiment, one ormore sensors of various types, depending on the application orimplementation requirements, as would be understood by one skilled inthe art. The sensors of the optional sensing component 2160 provide dataand/or information to at least the processing component 2110. In oneaspect, the processing component 2110 may be adapted to communicate withthe sensing component 2160 (e.g., by receiving sensor information fromthe sensing component 2160) and with the image capture component 2130(e.g., by receiving data and information from the image capturecomponent 2130 and providing and/or receiving command, control, and/orother information to and/or from one or more other components of thesystem 2100).

In various implementations, the sensing component 2160 may provideinformation regarding environmental conditions, such as outsidetemperature, lighting conditions (e.g., day, night, dusk, and/or dawn),humidity level, specific weather conditions (e.g., sun, rain, and/orsnow), distance (e.g., laser rangefinder), and/or whether a tunnel orother type of enclosure has been entered or exited. The sensingcomponent 2160 may represent conventional sensors as generally known byone skilled in the art for monitoring various conditions (e.g.,environmental conditions) that may have an effect (e.g., on the imageappearance) on the data provided by the image capture component 2130.

In some implementations, the optional sensing component 2160 (e.g., oneor more of sensors) may comprise devices that relay information to theprocessing component 2110 via wired and/or wireless communication. Forexample, the optional sensing component 2160 may be adapted to receiveinformation from a satellite, through a local broadcast (e.g., radiofrequency (RF)) transmission, through a mobile or cellular networkand/or through information beacons in an infrastructure (e.g., atransportation or highway information beacon infrastructure), or variousother wired and/or wireless techniques.

In various embodiments, components of the system 2100 may be combinedand/or implemented or not, as desired or depending on the application orrequirements, with the system 2100 representing various functionalblocks of a related system. In one example, the processing component2110 may be combined with the memory component 2120, the image capturecomponent 2130, the display component 2150, and/or the optional sensingcomponent 2160. In another example, the processing component 2110 may becombined with the image capture component 2130 with only certainfunctions of the processing component 2110 performed by circuitry (e.g.,a processor, a microprocessor, a logic device, a microcontroller, etc.)within the image capture component 2130. Furthermore, various componentsof the system 2100 may be remote from each other (e.g., image capturecomponent 2130 may comprise a remote sensor with processing component2110, etc. representing a computer that may or may not be incommunication with the image capture component 2130).

In accordance with an embodiment of the disclosure, FIG. 15A shows amethod 2220 for noise filtering an infrared image. In oneimplementation, this method 2220 relates to the reduction and/or removalof temporal, 1/f, and/or fixed spatial noise in infrared imagingdevices, such as infrared imaging system 2100 of FIG. 14. The method2220 is adapted to utilize the row and column based noise components ofinfrared image data in a noise filtering algorithm. In one aspect, therow and column based noise components may dominate the noise in imageryof infrared sensors (e.g., approximately ⅔ of the total noise may bespatial in a typical micro-bolometer based system).

In one embodiment, the method 2220 of FIG. 15A comprises a high levelblock diagram of row and column noise filtering algorithms. In oneaspect, the row and column noise filter algorithms may be optimized touse minimal hardware resources.

Referring to FIG. 15A, the process flow of the method 2220 implements arecursive mode of operation, wherein the previous correction terms areapplied before calculating row and column noise, which may allow forcorrection of lower spatial frequencies. In one aspect, the recursiveapproach is useful when row and column noise is spatially correlated.This is sometimes referred to as banding and, in the column noise case,may manifest as several neighboring columns being affected by a similaroffset error. When several neighbors used in difference calculations aresubject to similar error, the mean difference used to calculate theerror may be skewed, and the error may only be partially corrected. Byapplying partial correction prior to calculating the error in thecurrent frame, correction of the error may be recursively reduced untilthe error is minimized or eliminated. In the recursive case, if the HPFis not applied (block 2208), then natural gradients as part of the imagemay, after several iterations, be distorted when merged into the noisemodel. In one aspect, a natural horizontal gradient may appear as lowspatially correlated column noise (e.g., severe banding). In anotheraspect, the HPF may prevent very low frequency scene information tointerfere with the noise estimate and, therefore, limits the negativeeffects of recursive filtering.

Referring to method 2220 of FIG. 15A, infrared image data (e.g., a rawvideo source, such as from the image capture component 2130 of FIG. 14)is received as input video data (block 2200). Next, column correctionterms are applied to the input video data (block 2201), and rowcorrection terms are applied to the input video data (block 2202). Next,video data (e.g., “cleaned” video data) is provided as output video data(2219) after column and row corrections are applied to the input videodata. In one aspect, the term “cleaned” may refer to removing orreducing noise (blocks 2201, 2202) from the input video data via, e.g.,one or more embodiments of the noise filter algorithm.

Referring to the processing portion (e.g., recursive processing) of FIG.15A, a HPF is applied (block 2208) to the output video data 2219 viadata signal path 2219 a. In one implementation, the high pass filtereddata is separately provided to a column noise filter portion 2201 a anda row noise filter portion 2202 a.

Referring to the column noise filter portion 2201 a, the method 2220 maybe adapted to process the input video data 2200 and/or output video data2219 as follows:

1. Apply previous column noise correction terms to a current frame ascalculated in a previous frame (block 2201).

2. High pass filter the row of the current frame by subtracting theresult of a low pass filter (LPF) operation (block 2208), for example,as discussed in reference to FIGS. 16A-16C.

3. For each pixel, calculate a difference between a center pixel and oneor more (e.g., eight) nearest neighbors (block 2214). In oneimplementation, the nearest neighbors comprise one or more nearesthorizontal neighbors. The nearest neighbors may include one or morevertical or other non-horizontal neighbors (e.g., not pure horizontal,i.e., on the same row), without departing from the scope of theinvention.

4. If the calculated difference is below a predefined threshold, add thecalculated difference to a histogram of differences for the specificcolumn (block 2209).

5. At an end of the current frame, find a median difference by examininga cumulative histogram of differences (block 2210). In one aspect, foradded robustness, only differences with some specified minimum number ofoccurrences may be used.

6. Delay the current correction terms for one frame (block 2211), i.e.,they are applied to the next frame.

7. Add median difference (block 2212) to previous column correctionterms to provide updated column correction terms (block 2213).

8. Apply updated column noise correction terms in the next frame (block2201).

Referring to the row noise filter portion 2202 a, the method 2220 may beadapted to process the input video data 2200 and/or output video data2219 as follows:

1. Apply previous row noise correction terms to a current frame ascalculated in a previous frame (block 2202).

2. High pass filter the column of the current frame by subtracting theresult of a low pass filter (LPF) operation (block 2208), as discussedsimilarly above for column noise filter portion 2201 a.

3. For each pixel, calculate a difference between a center pixel and oneor more (e.g., eight) nearest neighbors (block 2215). In oneimplementation, the nearest neighbors comprise one or more nearestvertical neighbors. The nearest neighbors may include one or morehorizontal or other non-vertical neighbors (e.g., not pure vertical,i.e., on the same column), without departing from the scope of theinvention.

4. If the calculated difference is below a predefined threshold, add thecalculated difference to a histogram of differences for the specific row(block 2207).

5. At an end of the current row (e.g., line), find a median differenceby examining a cumulative histogram of differences (block 2206). In oneaspect, for added robustness only differences with some specifiedminimum number of occurrences may be used.

6. Delay the current frame by a time period equivalent to the number ofnearest vertical neighbors used, for example eight.

7. Add median difference (block 2204) to row correction terms (block2203) from previous frame (block 2205).

8. Apply updated row noise correction terms in the current frame (block2202). In one aspect, this may require a row buffer (e.g., as mentionedin 6).

In one aspect, for all pixels (or at least a large subset of them) ineach column, an identical offset term (or set of terms) may be appliedfor each associated column. This may prevent the filter from blurringspatially local details.

Similarly, in one aspect, for all pixels (or at least a large subset ofthem) in each row respectively, an identical offset term (or set ofterms) may be applied. This may inhibit the filter from blurringspatially local details.

In one example, an estimate of the column offset terms may be calculatedusing only a subset of the rows (e.g., the first 32 rows). In this case,only a 32 row delay is needed to apply the column correction terms inthe current frame. This may improve filter performance in removing hightemporal frequency column noise. Alternatively, the filter may bedesigned with minimum delay, and the correction terms are only appliedonce a reasonable estimate can be calculated (e.g., using data from the32 rows). In this case, only rows 33 and beyond may be optimallyfiltered.

In one aspect, all samples may not be needed, and in such an instance,only every 2nd or 4th row, e.g., may be used for calculating the columnnoise. In another aspect, the same may apply when calculating row noise,and in such an instance, only data from every 4th column, e.g., may beused. It should be appreciated that various other iterations may be usedby one skilled in the art without departing from the scope of theinvention.

In one aspect, the filter may operate in recursive mode in which thefiltered data is filtered instead of the raw data being filtered. Inanother aspect, the mean difference between a pixel in one row andpixels in neighboring rows may be approximated in an efficient way if arecursive (IIR) filter is used to calculate an estimated running mean.For example, instead of taking the mean of neighbor differences (e.g.,eight neighbor differences), the difference between a pixel and the meanof the neighbors may be calculated.

In accordance with an embodiment of the disclosure, FIG. 15B shows analternative method 2230 for noise filtering infrared image data. Inreference to FIGS. 15A and 15B, one or more of the process steps and/oroperations of method 2220 of FIG. 15A have changed order or have beenaltered or combined for the method 2230 of FIG. 15B. For example, theoperation of calculating row and column neighbor differences (blocks2214, 2215) may be removed or combined with other operations, such asgenerating histograms of row and column neighbor differences (blocks2207, 2209). In another example, the delay operation (block 2205) may beperformed after finding the median difference (block 2206). In variousexamples, it should be appreciated that similar process steps and/oroperations have similar scope, as previously described in FIG. 15A, andtherefore, the description will not be repeated.

In still other alternate approaches to methods 2220 and 2230,embodiments may exclude the histograms and rely on mean calculateddifferences instead of median calculated differences. In one aspect,this may be slightly less robust but may allow for a simplerimplementation of the column and row noise filters. For example, themean of neighboring rows and columns, respectively, may be approximatedby a running mean implemented as an infinite impulse response (IIR)filter. In the row noise case, the IIR filter implementation may reduceor even eliminate the need to buffer several rows of data for meancalculations.

In still other alternate approaches to methods 2220 and 2230, new noiseestimates may be calculated in each frame of the video data and onlyapplied in the next frame (e.g., after noise estimates). In one aspect,this alternate approach may provide less performance but may be easierto implement. In another aspect, this alternate approach may be referredto as a non-recursive method, as understood by those skilled in the art.

For example, in one embodiment, the method 2240 of FIG. 15C comprises ahigh level block diagram of row and column noise filtering algorithms.In one aspect, the row and column noise filter algorithms may beoptimized to use minimal hardware resources. In reference to FIGS. 15Aand 15B, similar process steps and/or operations may have similar scope,and therefore, the descriptions will not be repeated.

Referring to FIG. 15C, the process flow of the method 2240 implements anon-recursive mode of operation. As shown, the method 2240 appliescolumn offset correction term 2201 and row offset correction term 2202to the uncorrected input video data from video source 2200 to produce,e.g., a corrected or cleaned output video signal 2219. In column noisefilter portion 2201 a, column offset correction terms 2213 arecalculated based on the mean difference 2210 between pixel values in aspecific column and one or more pixels belonging to neighboring columns2214. In row noise filter portion 2202 a, row offset correction terms2203 are calculated based on the mean difference 2206 between pixelvalues in a specific row and one or more pixels belonging to neighboringrows 2215. In one aspect, the order (e.g., rows first or columns first)in which row or column offset correction terms 2203, 2213 are applied tothe input video data from video source 2200 may be considered arbitrary.In another aspect, the row and column correction terms may not be fullyknown until the end of the video frame, and therefore, if the inputvideo data from the video source 2200 is not delayed, the row and columncorrection terms 2203, 2213 may not be applied to the input video datafrom which they were calculated.

In one aspect of the invention, the column and row noise filteralgorithm may operate continuously on image data provided by an infraredimaging sensor (e.g., image capture component 2130 of FIG. 14). Unlikeconventional methods that may require a uniform scene (e.g., as providedby a shutter or external calibrated black body) to estimate the spatialnoise, the column and row noise filter algorithms, as set forth in oneor more embodiments, may operate on real-time scene data. In one aspect,an assumption may be made that, for some small neighborhood aroundlocation [x, y], neighboring infrared sensor elements should providesimilar values since they are imaging parts of the scene in closeproximity. If the infrared sensor reading from a particular infraredsensor element differs from a neighbor, then this could be the result ofspatial noise. However, in some instances, this may not be true for eachand every sensor element in a particular row or column (e.g., due tolocal gradients that are a natural part of the scene), but on average, arow or column may have values that are close to the values of theneighboring rows and columns.

For one or more embodiments, by first taking out one or more low spatialfrequencies (e.g., using a high pass filter (HPF)), the scenecontribution may be minimized to leave differences that correlate highlywith actual row and column spatial noise. In one aspect, by using anedge preserving filter, such as a Median filter or a Bilateral filter,one or more embodiments may minimize artifacts due to strong edges inthe image.

In accordance with one or more embodiments of the disclosure, FIGS. 16Ato 16C show a graphical implementation (e.g., digital counts versus datacolumns) of filtering an infrared image. FIG. 16A shows a graphicalillustration (e.g., graph 2300) of typical values, as an example, from arow of sensor elements when imaging a scene. FIG. 16B shows a graphicalillustration (e.g., graph 2310) of a result of a low pass filtering(LPF) of the image data values from FIG. 16A. FIG. 16C shows a graphicalillustration (e.g., graph 2320) of subtracting the low pass filter (LPF)output in FIG. 16B from the original image data in FIG. 16A, whichresults in a high pass filter (HPF) profile with low and mid frequencycomponents removed from the scene of the original image data in FIG.16A. Thus, FIG. 16A-16C illustrate a HPF technique, which may be usedfor one or more embodiments (e.g., as with methods 2220 and/or 2230).

In one aspect of the invention, a final estimate of column and/or rownoise may be referred to as an average or median estimate of all of themeasured differences. Because noise characteristics of an infraredsensor are often generally known, then one or more thresholds may beapplied to the noise estimates. For example, if a difference of 60digital counts is measured, but it is known that the noise typically isless than 10 digital counts, then this measurement may be ignored.

In accordance with one or more embodiments of the disclosure, FIG. 17shows a graphical illustration 2400 (e.g., digital counts versus datacolumns) of a row of sensor data 2401 (e.g., a row of pixel data for aplurality of pixels in a row) with column 5 data 2402 and data for eightnearest neighbors (e.g., nearest pixel neighbors, 4 columns 2410 to theleft of column 5 data 2402 and 4 columns 2411 to the right of column 5data 2402). In one aspect, referring to FIG. 17, the row of sensor data2401 is part of a row of sensor data for an image or scene captured by amulti-pixel infrared sensor or detector (e.g., image capture component2130 of FIG. 14). In one aspect, column 5 data 2402 is a column of datato be corrected. For this row of sensor data 2401, the differencebetween column 5 data 2402 and a mean 2403 of its neighbor columns(2410, 2411) is indicated by an arrow 2404. Therefore, noise estimatesmay be obtained and accounted for based on neighboring data.

In accordance with one or more embodiments of the disclosure, FIGS. 18Ato 18C show an exemplary implementation of column and row noisefiltering an infrared image (e.g., an image frame from infrared videodata). FIG. 18A shows an infrared image 2500 with column noise estimatedfrom a scene with severe row and column noise present and acorresponding graph 2502 of column correction terms. FIG. 18B shows aninfrared image 2510, with column noise removed and spatial row noisestill present, with row correction terms estimated from the scene inFIG. 18A and a corresponding graph 2512 of row correction terms. FIG.18C shows an infrared image 2520 of the scene in FIG. 18A as a cleanedinfrared image with row and column noise removed (e.g., column and rowcorrection terms of FIGS. 18A-18B applied).

In one embodiment, FIG. 18A shows an infrared video frame (i.e.,infrared image 2500) with severe row and column noise. Column noisecorrection coefficients are calculated as described herein to produce,e.g., 639 correction twins, i.e., one correction term per column. Thegraph 2502 shows the column correction terms. These offset correctionterms are subtracted from the infrared video frame 2500 of FIG. 18A toproduce the infrared image 2510 in FIG. 18B. As shown in FIG. 18B, therow noise is still present. Row noise correction coefficients arecalculated as described herein to produce, e.g., 639 row terms, i.e.,one correction term per row. The graph 2512 shows the row offsetcorrection terms, which are subtracted from the infrared image 2510 inFIG. 18B to produce the cleaned infrared image 2520 in FIG. 18C withsignificantly reduced or removed row and column noise.

In various embodiments, it should be understood that both row and columnfiltering is not required. For example, either column noise filtering2201 a or row noise filtering 2202 a may be performed in methods 2220,2230 or 2240.

It should be appreciated that any reference to a column or a row mayinclude a partial column or a partial row and that the terms “row” and“column” are interchangeable and not limiting. For example, withoutdeparting from the scope of the invention, the term “row” may be used todescribe a row or a column, and likewise, the term “column” may be usedto describe a row or a column, depending upon the application.

In various aspects, column and row noise may be estimated by looking ata real scene (e.g., not a shutter or a black body), in accordance withembodiments of the noise filtering algorithms, as disclosed herein. Thecolumn and row noise may be estimated by measuring the median or meandifference between sensor readings from elements in a specific row(and/or column) and sensor readings from adjacent rows (and/or columns).

Optionally, a high pass filter may be applied to the image data prior tomeasuring the differences, which may reduce or at least minimize a riskof distorting gradients that are part of the scene and/or introducingartifacts. In one aspect, only sensor readings that differ by less thana configurable threshold may be used in the mean or median estimation.Optionally, a histogram may be used to effectively estimate the median.Optionally, only histogram bins exceeding a minimum count may be usedwhen finding the median estimate from the histogram. Optionally, arecursive IIR filter may be used to estimate the difference between apixel and its neighbors, which may reduce or at least minimize the needto store image data for processing, e.g., the row noise portion (e.g.,if image data is read out row wise from the sensor). In oneimplementation, the current mean column value C _(i,j) for column i atrow j may be estimated using the following recursive filter algorithm.

${\overset{\_}{C}}_{i,j} = {{\left( {1 - \alpha} \right) \cdot {\overset{\_}{C}}_{{i - 1},j}} + {\alpha \cdot C_{i,j}}}$${\Delta\; R_{i}} = {{\frac{1}{N}{\sum\limits_{j = 1}^{N}\; C_{i,j}}} - {\overset{\_}{C}}_{{i - 1},j}}$

In this equation a is the damping factor and may be set to for example0.2 in which case the estimate for the running mean of a specific columni at row j will be a weighted sum of the estimated running mean forcolumn i−1 at row j and the current pixel value at row j and column i.The estimated difference between values of row j and the values ofneighboring rows can now be approximated by taking the difference ofeach value C_(i,j) and the running recursive mean of the neighbors aboverow i(C _(j-1,j)) Estimating the mean difference this way is not asaccurate as taking the true mean difference since only rows above areused but it requires that only one row of running means are stored ascompared to several rows of actual pixel values be stored.

In one embodiment, referring to FIG. 15A, the process flow of method2220 may implement a recursive mode of operation, wherein the previouscolumn and row correction terms are applied before calculating row andcolumn noise, which allows for correction of lower spatial frequencieswhen the image is high pass filtered prior to estimating the noise.

Generally, during processing, a recursive filter re-uses at least aportion of the output data as input data. The feedback input of therecursive filter may be referred to as an infinite impulse response(IIR), which may be characterized, e.g., by exponentially growing outputdata, exponentially decaying output data, or sinusoidal output data. Insome implementations, a recursive filter may not have an infiniteimpulse response. As such, e.g., some implementations of a movingaverage filter function as recursive filters but with a finite impulseresponse (FIR).

As further set forth in the description of FIGS. 19A to 22B, additionaltechniques are contemplated to determine row and/or column correctionterms. For example, in some embodiments, such techniques may be used toprovide correction terms without overcompensating for the presence ofvertical and/or horizontal objects present in scene 2170. Suchtechniques may be used in any appropriate environment where such objectsmay be frequently captured including, for example, urban applications,rural applications, vehicle applications, and others. In someembodiments, such techniques may provide correction terms with reducedmemory and/or reduced processing overhead in comparison with otherapproaches used to determine correction terms.

FIG. 19A shows an infrared image 2600 (e.g., infrared image data) ofscene 2170 in accordance with an embodiment of the disclosure. Althoughinfrared image 2600 is depicted as having 16 rows and 16 columns, otherimage sizes are contemplated for infrared image 2600 and the variousother infrared images discussed herein. For example, in one embodiment,infrared image 2600 may have 640 columns and 512 rows.

In FIG. 19A, infrared image 2600 depicts scene 2170 as relativelyuniform, with a majority of pixels 2610 of infrared image 2600 havingthe same or similar intensity (e.g., the same or similar numbers ofdigital counts). Also in this embodiment, scene 2170 includes an object2621 which appears in pixels 2622A-D of a column 2620A of infrared image2600. In this regard, pixels 2622A-D are depicted somewhat darker thanother pixels 2610 of infrared image 2600. For purposes of discussion, itwill be assumed that darker pixels are associated with higher numbers ofdigital counts, however lighter pixels may be associated with highernumbers of digital counts in other implementations if desired. As shown,the remaining pixels 2624 of column 2620A have a substantially uniformintensity with pixels 2610.

In some embodiments, object 2621 may be a vertical object such as abuilding, telephone pole, light pole, power line, cellular tower, tree,human being, and/or other object. If image capture component 2130 isdisposed in a vehicle approaching object 2621, then object 2621 mayappear relatively fixed in infrared image 2600 while the vehicle isstill sufficiently far away from object 2621 (e.g., object 2621 mayremain primarily represented by pixels 2622A-D and may not significantlyshift position within infrared image 2600). If image capture component2130 is disposed at a fixed location relative to object 2621, thenobject 2621 may also appear relatively fixed in infrared image 2600(e.g., if object 2621 is fixed and/or is positioned sufficiently faraway). Other dispositions of image capture component 2130 relative toobject 2621 are also contemplated.

Infrared image 2600 also includes another pixel 2630 which may beattributable to, for example, temporal noise, fixed spatial noise, afaulty sensor/circuitry, actual scene information, and/or other sources.As shown in FIG. 19A, pixel 2630 is darker (e.g., has a higher number ofdigital counts) than all of pixels 2610 and 2622A-D.

Vertical objects such as object 2621 depicted by pixels 2622A-D areoften problematic for some column correction techniques. In this regard,objects that remain disposed primarily in one or several columns mayresult in overcompensation when column correction terms are calculatedwithout regard to the possible presence of small vertical objectsappearing in scene 2170. For example, when pixels 2622A-D of column2620A are compared with those of nearby columns 2620B-E, some columncorrection techniques may interpret pixels 2622A-D as column noise,rather than actual scene information. Indeed, the significantly darkerappearance of pixels 2622A-D relative to pixels 2610 and the relativelysmall width of object 2621 disposed in column 2620A may skew thecalculation of a column correction term to significantly correct theentire column 2620A, although only a small portion of column 2620Aactually includes darker scene information. As a result, the columncorrection term determined for column 2620A may significantly lighten(e.g., brighten or reduce the number of digital counts) column 2620A tocompensate for the assumed column noise.

For example, FIG. 19B shows a corrected version 2650 of infrared image2600 of FIG. 19A. As shown in FIG. 19B, column 2620A has beensignificantly brightened. Pixels 2622A-D have been made significantlylighter to be approximately uniform with pixels 2610, and the actualscene information (e.g., the depiction of object 2621) contained inpixels 2622A-D has been mostly lost. In addition, remaining pixels 2624of column 2620A have been significantly brightened such that they are nolonger substantially uniform with pixels 2610. Indeed, the columncorrection term applied to column 2620A has actually introduced newnon-uniformities in pixels 2624 relative to the rest of scene 2170.

Various techniques described herein may be used to determine columncorrection terms without overcompensating for the appearance of variousvertical objects that may be present in scene 2170. For example, in oneembodiment, when such techniques are applied to column 2620A of FIG.19A, the presence of dark pixels 2622A-D may not cause any furtherchanges to the column correction term for column 2620A (e.g., aftercorrection is applied, column 2620A may appear as shown in FIG. 19Arather than as shown in FIG. 19B).

In accordance with various embodiments further described herein,corresponding column correction terms may be determined for each columnof an infrared image without overcompensating for the presence ofvertical objects present in scene 2170. In this regard, a first pixel ofa selected column of an infrared image (e.g., the pixel of the columnresiding in a particular row) may be compared with a corresponding setof other pixels (e.g., also referred to as neighborhood pixels) that arewithin a neighborhood associated with the first pixel. In someembodiments, the neighborhood may correspond to pixels in the same rowas the first pixel that are within a range of columns. For example, theneighborhood may be defined by an intersection of: the same row as thefirst pixel; and a predetermined range of columns.

The range of columns may be any desired number of columns on the leftside, right side, or both left and right sides of the selected column.In this regard, if the range of columns corresponds to two columns onboth sides of the selected column, then four comparisons may be made forthe first pixel (e.g., two columns to the left of the selected column,and two columns to the right of the selected column). Although a rangeof two columns on both sides of the selected column is further describedherein, other ranges are also contemplated (e.g., 5 columns, 8 columns,or any desired number of columns).

One or more counters (e.g., registers, memory locations, accumulators,and/or other implementations in processing component 2110, noisefiltering module 2112, memory component 2120, and/or other components)are adjusted (e.g., incremented, decremented, or otherwise updated)based on the comparisons. In this regard, for each comparison where thepixel of the selected column has a lesser value than a compared pixel, acounter A may be adjusted. For each comparison where the pixel of theselected column has an equal (e.g., exactly equal or substantiallyequal) value as a compared pixel, a counter B may be adjusted. For eachcomparison where the pixel of the selected column has a greater valuethan a compared pixel, a counter C may be adjusted. Thus, if the rangeof columns corresponds to two columns on either side of the selectedcolumn as identified in the example above, then a total of fouradjustments (e.g., counts) may be collectively held by counters A, B,and C.

After the first pixel of the selected column is compared with all pixelsin its corresponding neighborhood, the process is repeated for allremaining pixels in the selected column (e.g., one pixel for each row ofthe infrared image), and counters A, B, and C continue to be adjusted inresponse to the comparisons performed for the remaining pixels. In thisregard, in some embodiments, each pixel of the selected column may becompared with a different corresponding neighborhood of pixels (e.g.,pixels residing: in the same row as the pixel of the selected column;and within a range of columns), and counters A, B, and C may be adjustedbased on the results of such comparisons.

As a result, after all pixels of the selected column are compared,counters A, B, and C may identify the number of comparisons for whichpixels of the selected column were found to be greater, equal, or lessthan neighborhood pixels. Thus, continuing the example above, if theinfrared image has 16 rows, then a total of 64 counts may be distributedacross counters A, B, and C for the selected column (e.g., 4 counts perrow×16 rows=64 counts). It is contemplated that other numbers of countsmay be used. For example, in a large array having 512 rows and using arange of 10 columns, 5120 counts (e.g., 512 rows×10 columns) may be usedto determine each column correction term.

Based on the distribution of the counts in counters A, B, and C, thecolumn correction term for the selected column may be selectivelyincremented, decremented, or remain the same based on one or morecalculations performed using values of one or more of counters A, B,and/or C. For example, in some embodiments: the column correction termmay be incremented if counter A−counter B−counter C>D; the columncorrection term may be decremented if counter C−counter A−counter B>D;and the column correction term may remain the same in all other cases.In such embodiments, D may be a value such as a constant value smallerthan the total number of comparisons accumulated by counters A, B, and Cper column. For example, in one embodiment, D may have a value equal to:(number of rows)/2.

The process may be repeated for all remaining columns of the infraredimage in order to determine (e.g., calculate and/or update) acorresponding column correction term for each column of the infraredimage. In addition, after column correction terms have been determinedfor one or more columns, the process may be repeated for one or morecolumns (e.g., to increment, decrement, or not change one or more columncorrection terms) after the column corrected terms are applied to thesame infrared image and/or another infrared image (e.g., a subsequentlycaptured infrared image).

As discussed, counters A, B, and C identify the number of comparedpixels that are less than, equal to, or greater than pixels of theselected column. This contrasts with various other techniques used todetermine column correction terms where the actual differences (e.g.,calculated difference values) between compared pixels may be used.

By determining column correction terms based on less than, equal to, orgreater than relationships (e.g., rather than the actual numericaldifferences between the digital counts of different pixels), the columncorrection terms may be less skewed by the presence of small verticalobjects appearing in infrared images. In this regard, by using thisapproach, small objects such as object 2621 with high numbers of digitalcounts may not inadvertently cause column correction terms to becalculated that would overcompensate for such objects (e.g., resultingin an undesirable infrared image 2650 as shown in FIG. 19B). Rather,using this approach, object 2621 may not cause any change to columncorrection terms (e.g., resulting in an unchanged infrared image 2600 asshown in FIG. 19A). However, larger objects such as object 2721 whichmay be legitimately identified as column noise may be appropriatelyreduced through adjustment of column correction terms (e.g., resultingin a corrected infrared image 2750 as shown in FIG. 20B).

In addition, using this approach may reduce the effects of other typesof scene information on column correction term values. In this regard,counters A, B, and C identify relative relationships (e.g., less than,equal to, or greater than relationships) between pixels of the selectedcolumn and neighborhood pixels. In some embodiments, such relativerelationships may correspond, for example, to the sign (e.g., positive,negative, or zero) of the difference between the values of pixels of theselected column and the values of neighborhood pixels. By using relativerelationships rather than actual numerical differences, exponentialscene changes (e.g., non-linear scene information gradients) maycontribute less to column correction term determinations. For example,exponentially higher digital counts in certain pixels may be treated assimply being greater than or less than other pixels for comparisonpurposes and consequently will not unduly skew the column correctionterm.

In addition, by identifying relative relationships rather than actualnumerical differences in counters A, B, and C, high pass filtering canbe reduced in some embodiments. In this regard, where low frequencyscene information or noise remains fairly uniform throughout comparedneighborhoods of pixels, such low frequency content may notsignificantly affect the relative relationships between the comparedpixels.

Advantageously, counters A, B, and C provide an efficient approach tocalculating column correction terms. In this regard, in someembodiments, only three counters A, B, and C are used to store theresults of all pixel comparisons performed for a selected column. Thiscontrasts with various other approaches in which many more unique valuesare stored (e.g., where particular numerical differences, or the numberof occurrences of such numerical differences, are stored).

In some embodiments, where the total number of rows of an infrared imageis known, further efficiency may be achieved by omitting counter B. Inthis regard, the total number of counts may be known based on the rangeof columns used for comparison and the number of rows of the infraredimage. In addition, it may be assumed that any comparisons that do notresult in counter A or counter C being adjusted will correspond to thosecomparisons where pixels have equal values. Therefore, the value thatwould have been held by counter B may be determined from counters A andC (e.g., (number of rows×range)−counter A value−counter B value=counterC value).

In some embodiments, only a single counter may be used. In this regard,a single counter may be selectively adjusted in a first manner (e.g.,incremented or decremented) for each comparison where the pixel of theselected column has a greater value than a compared pixel, selectivelyadjusted in a second manner (e.g., decremented or incremented) for eachcomparison where the pixel of the selected column has a lesser valuethan a compared pixel, and not adjusted (e.g., retaining its existingvalue) for each comparison where the pixel of the selected column has anequal (e.g., exactly equal or substantially equal) value as a comparedpixel. Thus, the value of the single counter may indicate relativenumbers of compared pixels that are greater than or less than the pixelsof the selected column (e.g., after all pixels of the selected columnhave been compared with corresponding neighborhood pixels).

A column correction term for the selected column may be updated (e.g.,incremented, decremented, or remain the same) based on the value of thesingle counter. For example, in some embodiments, if the single counterexhibits a baseline value (e.g., zero or other number) after comparisonsare performed, then the column correction term may remain the same. Insome embodiments, if the single counter is greater or less than thebaseline value, the column correction term may be selectivelyincremented or decremented as appropriate to reduce the overalldifferences between the compared pixels and the pixels of the selectedcolumn. In some embodiments, the updating of the column correction termmay be conditioned on the single counter having a value that differsfrom the baseline value by at least a threshold amount to prevent undueskewing of the column correction term based on limited numbers ofcompared pixels having different values from the pixels of the selectedcolumn.

These techniques may also be used to compensate for larger verticalanomalies in infrared images where appropriate. For example, FIG. 20Aillustrates an infrared image 2700 of scene 2170 in accordance with anembodiment of the disclosure. Similar to infrared image 2600, infraredimage 2700 depicts scene 2170 as relatively uniform, with a majority ofpixels 2710 of infrared image 2700 having the same or similar intensity.Also in this embodiment, a column 2720A of infrared image 2700 includespixels 2722A-M that are somewhat darker than pixels 2710, while theremaining pixels 2724 of column 2720A have a substantially uniformintensity with pixels 2710.

However, in contrast to pixels 2622A-D of FIG. 19A, pixels 2722A-M ofFIG. 20A occupy a significant majority of column 2720A. As such, it ismore likely that an object 2721 depicted by pixels 2722A-M may actuallybe an anomaly such as column noise or another undesired source ratherthan an actual structure or other actual scene information. For example,in some embodiments, it is contemplated that actual scene informationthat occupies a significant majority of at least one column would alsolikely occupy a significant horizontal portion of one or more rows. Forexample, a vertical structure in close proximity to image capturecomponent 2130 may be expected to occupy multiple columns and/or rows ofinfrared image 2700. Because object 2721 appears as a tall narrow bandoccupying a significant majority of only one column 2720A, it is morelikely that object 2721 is actually column noise.

FIG. 20B shows a corrected version 2750 of infrared image 2700 of FIG.20A. As shown in FIG. 20B, column 2720A has been brightened, but not assignificantly as column 2620A of infrared image 2650. Pixels 2722A-Mhave been made lighter, but still appear slightly darker than pixels2710. In another embodiment, column 2720A may be corrected such thatpixels 2722A-M may be approximately uniform with pixels 2710. As alsoshown in FIG. 20B, remaining pixels 2724 of column 2720A have beenbrightened but not as significantly as pixels 2624 of infrared image2650. In another embodiment, pixels 2724 may be further brightened ormay remain substantially uniform with pixels 2710.

Various aspects of these techniques are further explained with regard toFIGS. 21 and 22A-B. In this regard, FIG. 21 is a flowchart illustratinga method 2800 for noise filtering an infrared image, in accordance withan embodiment of the disclosure. Although particular components ofsystem 2100 are referenced in relation to particular blocks of FIG. 21,the various operations described with regard to FIG. 21 may be performedby any appropriate components, such as image capture component 2130,processing component, 2110, noise filtering module 2112, memorycomponent 2120, control component 2140, and/or others.

In block 2802, image capture component 2130 captures an infrared image(e.g., infrared image 2600 or 2700) of scene 2170. In block 2804, noisefiltering module 2112 applies existing row and column correction termsto infrared image 2600/2700. In some embodiments, such existing row andcolumn correction terms may be determined by any of the varioustechniques described herein, factory calibration operations, and/orother appropriate techniques. In some embodiments, the column correctionterms applied in block 2804 may be undetermined (e.g., zero) during afirst iteration of block 2804, and may be determined and updated duringone or more iterations of FIG. 21.

In block 2806, noise filtering module 2112 selects a column of infraredimage 2600/2700. Although column 2620A/2720A will be referenced in thefollowing description, any desired column may be used. For example, insome embodiments, a rightmost or leftmost column of infrared image2600/2700 may be selected in a first iteration of block 2806. In someembodiments, block 2806 may also include resetting counters A, B, and Cto zero or another appropriate default value.

In block 2808, noise filtering module 2112 selects a row of infraredimage 2600/2700. For example, a topmost row 2601A/2701A of infraredimage 2600/2700 may be selected in a first iteration of block 2808.Other rows may be selected in other embodiments.

In block 2810, noise filtering module 2112 selects another column in aneighborhood for comparison to column 2620A. In this example, theneighborhood has a range of two columns (e.g., columns 2620B-E/2720B-E)on both sides of column 2620A/2720A, corresponding to pixels2602B-E/2702B-E in row 2601A/2701A on either side of pixel 2602A/2702A.Accordingly, in one embodiment, column 2620B/2720B may be selected inthis iteration of block 2810.

In block 2812, noise filtering module 2112 compares pixels 2602B/2702Bto pixel 2602A/2702A. In block 2814, counter A is adjusted if pixel2602A/2702A has a lower value than pixel 2602B/2702B. Counter B isadjusted if pixel 2602A/2702A has an equal value as pixel 2602B/2702B.Counter C is adjusted if pixel 2602A/2702A has a higher value than pixel2602B/2702B. In this example, pixel 2602A/2702A has an equal value aspixel 2602B/2702B. Accordingly, counter B will be adjusted, and countersA and C will not be adjusted in this iteration of block 2814.

In block 2816, if additional columns in the neighborhood remain to becompared (e.g., columns 2620C-E/2720C-E), then blocks 2810-2816 arerepeated to compare the remaining pixels of the neighborhood (e.g.,pixels 2602B-E/2702B-E residing in columns 2620C-E/2720C-E and in row2601A/2701A) to pixel 2602A/2702A. In FIGS. 19A/20A, pixel 2602A/2702Ahas an equal value as all of pixels 2602B-E/2702B-E. Accordingly, afterpixel 2602A/2702A has been compared with all pixels in its neighborhood,counter B will have been adjusted by four counts, and counters A and Cwill not have been adjusted.

In block 2818, if additional rows remain in infrared images 2600/2700(e.g., rows 2601B-P/2701B-P), then blocks 2808-2818 are repeated tocompare the remaining pixels of column 2620A/2720A with the remainingpixels of columns 2602B-E/2702B-E on a row by row basis as discussedabove.

Following block 2818, each of the 16 pixels of column 2620A/2720A willhave been compared to 4 pixels (e.g., pixels in columns 2620B-E residingin the same row as each compared pixel of column 2620A/2720A) for atotal of 64 comparisons. This results in 64 adjustments collectivelyshared by counters A, B, and C.

FIG. 22A shows the values of counters A, B, and C represented by ahistogram 2900 after all pixels of column 2620A have been compared tothe various neighborhoods of pixels included in columns 2620B-E, inaccordance with an embodiment of the disclosure. In this case, countersA, B, and C have values of 1, 48, and 15, respectively. Counter A wasadjusted only once as a result of pixel 2622A of column 2620A having alower value than pixel 2630 of column 2620B. Counter C was adjusted 15times as a result of pixels 2622A-D each having a higher value whencompared to their neighborhood pixels of columns 2620B-E (e.g., exceptfor pixel 2630 as noted above). Counter B was adjusted 48 times as aresult of the remaining pixels 2624 of column 2620A having equal valuesas the remaining neighborhood pixels of columns 2620B-E.

FIG. 22B shows the values of counters A, B, and C represented by ahistogram 2950 after all pixels of column 2720A have been compared tothe various neighborhoods of pixels included in columns 2720B-E, inaccordance with an embodiment of the disclosure. In this case, countersA, B, and C have values of 1, 12, and 51, respectively. Similar to FIG.22A, counter A in FIG. 22B was adjusted only once as a result of a pixel2722A of column 2720A having a lower value than pixel 2730 of column2720B. Counter C was adjusted 51 times as a result of pixels 2722A-Meach having a higher value when compared to their neighborhood pixels ofcolumns 2720B-E (e.g., except for pixel 2730 as noted above). Counter Bwas adjusted 12 times as a result of the remaining pixels of column2720A having equal values as the remaining neighborhood compared pixelsof columns 2720B-E.

Referring again to FIG. 21, in block 2820, the column correction termfor column 2620A/2720A is updated (e.g., selectively incremented,decremented, or remain the same) based on the values of counters A, B,and C. For example, as discussed above, in some embodiments, the columncorrection term may be incremented if counter A−counter B−counter C>D;the column correction term may be decremented if counter C−counterA−counter B>D; and the column correction term may remain the same in allother cases.

In the case of infrared image 2600, applying the above calculations tothe counter values identified in FIG. 22A results in no change to thecolumn correction term (e.g., 1(counter A)−48(counter B)−15(counterC)=−62 which is not greater than D, where D equals (16 rows)/2; and15(counter C)−1(counter A)−48(counter B)=−34 which is not greater thanD, where D equals (16 rows)/2). Thus, in this case, the values ofcounters A, B, and C, and the calculations performed thereon indicatethat values of pixels 2622A-D are associated with an actual object(e.g., object 2621) of scene 2170. Accordingly, the small verticalstructure 2621 represented by pixels 2622A-D will not result in anyovercompensation in the column correction term for column 2620A.

In the case of infrared image 2700, applying the above calculations tothe counter values identified in FIG. 22B results in a decrement in thecolumn correction term (e.g., 51(counter C)−1(counter A)−12(counterB)=38 which is greater than D, where D equals (16 rows)/2). Thus, inthis case, the values of counters A, B, and C, and the calculationsperformed thereon indicate that the values of pixels 2722A-M areassociated with column noise. Accordingly, the large vertical object2721 represented by pixels 2722A-M will result in a lightening of column2720A to improve the uniformity of corrected infrared image 2750 shownin FIG. 20B.

At block 2822, if additional columns remain to have their columncorrection terms updated, then the process returns to block 2806 whereinblocks 2806-2822 are repeated to update the column correction term ofanother column. After all column correction terms have been updated, theprocess returns to block 2802 where another infrared image is captured.In this manner, FIG. 21 may be repeated to update column correctionterms for each newly captured infrared image.

In some embodiments, each newly captured infrared image may not differsubstantially from recent preceding infrared images. This may be due to,for example, a substantially static scene 2170, a slowing changing scene2170, temporal filtering of infrared images, and/or other reasons. Inthese cases, the accuracy of column correction terms determined by FIG.21 may improve as they are selectively incremented, decremented, orremain unchanged in each iteration of FIG. 21. As a result, in someembodiments, many of the column correction terms may eventually reach asubstantially steady state in which they remain relatively unchangedafter a sufficient number of iterations of FIG. 21, and while theinfrared images do not substantially change.

Other embodiments are also contemplated. For example, block 2820 may berepeated multiple times to update one or more column correction termsusing the same infrared image for each update. In this regard, after oneor more column correction terms are updated in block 2820, the processof FIG. 21 may return to block 2804 to apply the updated columncorrection terms to the same infrared image used to determine theupdated column correction terms. As a result, column correction termsmay be iteratively updated using the same infrared image. Such anapproach may be used, for example, in offline (non-realtime) processingand/or in realtime implementations with sufficient processingcapabilities.

In addition, any of the various techniques described with regard toFIGS. 19A-22B may be combined where appropriate with the othertechniques described herein. For example, some or all portions of thevarious techniques described herein may be combined as desired toperform noise filtering.

Although column correction terms have been primarily discussed withregard to FIGS. 19A-22B, the described techniques may be applied torow-based processing. For example, such techniques may be used todetermine and update row correction terms without overcompensating forsmall horizontal structures appearing in scene 2170, while alsoappropriately compensating for actual row noise. Such row-basedprocessing may be performed in addition to, or instead of variouscolumn-based processing described herein. For example, additionalimplementations of counters A, B, and/or C may be provided for suchrow-based processing.

In some embodiments where infrared images are read out on a row-by-rowbasis, row-corrected infrared images may be may be rapidly provided asrow correction terms are updated. Similarly, in some embodiments whereinfrared images are read out on a column-by-column basis,column-corrected infrared images may be may be rapidly provided ascolumn correction terms are updated.

Referring now to FIGS. 23A-E, as discussed, in some embodiments thetechniques described with regard to FIGS. 23A-E may be used in place ofand/or in addition to one or more operations of blocks 565-573 (seeFIGS. 5 and 8) to estimate FPN and/or determine NUC terms (e.g., flatfield correction terms). For example, in some embodiments, suchtechniques may be used to determine NUC terms to correct for spatiallycorrelated FPN and/or spatially uncorrelated (e.g., random) FPN withoutrequiring high pass filtering.

FIG. 23A illustrates an infrared image 3000 (e.g., infrared image data)of scene 2170 in accordance with an embodiment of the disclosure.Although infrared image 3000 is depicted as having 16 rows and 16columns, other image sizes are contemplated for infrared image 3000 andthe various other infrared images discussed herein.

In FIG. 23A, infrared image 3000 depicts scene 2170 as relativelyuniform, with a majority of pixels 3010 of infrared image 3000 havingthe same or similar intensity (e.g., the same or similar numbers ofdigital counts). Also in this embodiment, infrared image 3000 includespixels 3020 which are depicted somewhat darker than other pixels 3010 ofinfrared image 3000, and pixels 3030 which are depicted somewhatlighter. As previously mentioned, for purposes of discussion, it will beassumed that darker pixels are associated with higher numbers of digitalcounts, however lighter pixels may be associated with higher numbers ofdigital counts in other implementations if desired.

In some embodiments, infrared image 3000 may be an image frame receivedat block 560 and/or block 565 of FIGS. 5 and 8 previously describedherein. In this regard, infrared image 3000 may be an intentionallyblurred image frame provided by block 555 and/or 560 in which much ofthe high frequency content has already been filtered out due to, forexample, temporal filtering, defocusing, motion, accumulated imageframes, and/or other techniques as appropriate. As such, in someembodiments, any remaining high spatial frequency content (e.g.,exhibited as areas of contrast or differences in the blurred imageframe) remaining in infrared image 3000 may be attributed to spatiallycorrelated FPN and/or spatially uncorrelated FPN.

As such, it can be assumed that substantially uniform pixels 3010generally correspond to blurred scene information, and pixels 3020 and3030 correspond to FPN. For example, as shown in FIG. 23A, pixels 3020and 3030 are arranged in several groups, each of which is positioned ina general area of infrared image 3000 that spans multiple rows andcolumns, but is not correlated to a single row or column.

Various techniques described herein may be used to determine NUC termswithout overcompensating for the presence of nearby dark or lightpixels. As will be further described herein, when such techniques areused to determine NUC terms for individual pixels (e.g., 3040, 3050, and3060) of infrared image 3000, appropriate NUC terms may be determined tocompensate for FPN where appropriate in some cases withoutovercompensating for FPN in other cases.

In accordance with various embodiments further described herein, acorresponding NUC term may be determined for each pixel of an infraredimage. In this regard, a selected pixel of the infrared image may becompared with a corresponding set of other pixels (e.g., also referredto as neighborhood pixels) that are within a neighborhood associatedwith the selected pixel. In some embodiments, the neighborhood maycorrespond to pixels within a selected distance (e.g., within a selectedkernel size) of the selected pixel (e.g., an N by N neighborhood ofpixels around and/or adjacent to the selected pixel). For example, insome embodiments, a kernel of 5 may be used, but larger and smallersizes are also contemplated.

As similarly discussed with regard to FIGS. 19A-22B, one or morecounters (e.g., registers, memory locations, accumulators, and/or otherimplementations in processing component 2110, noise filtering module2112, memory component 2120, and/or other components) are adjusted(e.g., incremented, decremented, or otherwise updated) based on thecomparisons. In this regard, for each comparison where the selectedpixel has a lesser value than a compared pixel of the neighborhood, acounter E may be adjusted. For each comparison where the selected pixelhas an equal (e.g., exactly equal or substantially equal) value as acompared pixel of the neighborhood, a counter F may be adjusted. Foreach comparison where the selected pixel has a greater value than acompared pixel of the neighborhood, a counter G may be adjusted. Thus,if the neighborhood uses a kernel of 5, then a total of 24 comparisonsmay be made between the selected pixel and its neighborhood pixels.Accordingly, a total of 24 adjustments (e.g., counts) may becollectively held by counters E, F, and G. In this regard, counters E,F, and G may identify the number of comparisons for which neighborhoodpixels were greater, equal, or less than the selected pixel.

After the selected pixel has been compared to all pixels in itsneighborhood, a NUC term may be determined (e.g., adjusted) for thepixel based on the values of counters E, F, and G. Based on thedistribution of the counts in counters E, F, and G, the NUC term for theselected pixel may be selectively incremented, decremented, or remainthe same based on one or more calculations performed using values of oneor more of counters E, F, and/or G.

Such adjustment of the NUC term may be performed in accordance with anydesired calculation. For example, in some embodiments, if counter F issignificantly larger than counters E and G or above a particularthreshold value (e.g., indicating that a large number of neighborhoodpixels are exactly equal or substantially equal to the selected pixel),then it may be decided that the NUC term should remain the same. In thiscase, even if several neighborhood pixels exhibit values that aresignificantly higher or lower than the selected pixel, thoseneighborhood pixels will not skew the NUC term as might occur in othermean-based or median-based calculations.

As another example, in some embodiments, if counter E or counter G isabove a particular threshold value (e.g., indicating that a large numberof neighborhood pixels are greater than or less than the selectedpixel), then it may be decided that the NUC term should be incrementedor decremented as appropriate. In this case, because the NUC term may beincremented or decremented based on the number of neighborhood pixelsgreater, equal, or less than the selected pixel (e.g., rather than theactual pixel values of such neighborhood pixels), the NUC term may beadjusted in a gradual fashion without introducing rapid changes that mayinadvertently overcompensate for pixel value differences.

The process may be repeated by resetting counters E, F, and G, selectinganother pixel of infrared image 3000, performing comparisons with itsneighborhood pixels, and determining its NUC term based on the newvalues of counters E, F, and G. These operations can be repeated asdesired until a NUC term has been determined for every pixel of infraredimage 3000.

In some embodiments, after NUC terms have been determined for allpixels, the process may be repeated to further update the NUC termsusing the same infrared image 3000 (e.g., after application of the NUCterms) and/or another infrared image (e.g., a subsequently capturedinfrared image).

As discussed, counters E, F, and G identify the number of neighborhoodpixels that are greater than, equal to, or less than the selected pixel.This contrasts with various other techniques used to determine NUC termswhere the actual differences (e.g., calculated difference values)between compared pixels may be used.

Counters E, F, and G identify relative relationships (e.g., less than,equal to, or greater than relationships) between the selected pixel andits neighborhood pixels. In some embodiments, such relativerelationships may correspond, for example, to the sign (e.g., positive,negative, or zero) of the difference between the values of the selectedpixel and its neighborhood pixels. By determining NUC terms based onrelative relationships rather than actual numerical differences, the NUCterms may not be skewed by small numbers of neighborhood pixels havingdigital counts that widely diverge from the selected pixel.

In addition, using this approach may reduce the effects of other typesof scene information on NUC term values. In this regard, becausecounters E, F, and G identify relative relationships between pixelsrather than actual numerical differences, exponential scene changes(e.g., non-linear scene information gradients) may contribute less toNUC term determinations. For example, exponentially higher digitalcounts in certain pixels may be treated as simply being greater than orless than other pixels for comparison purposes and consequently will notunduly skew the NUC term. Moreover, this approach may be used withoutunintentionally distorting infrared images exhibiting a nonlinear slope.

Advantageously, counters E, F, and G provide an efficient approach tocalculating NUC terms. In this regard, in some embodiments, only threecounters E, F, and G are used to store the results of all neighborhoodpixel comparisons performed for a selected pixel. This contrasts withvarious other approaches in which many more unique values are stored(e.g., where particular numerical differences, or the number ofoccurrences of such numerical differences, are stored), median filtersare used (e.g., which may require sorting and the use of high pass orlow pass filters including a computationally intensive divide operationto obtain a weighted mean of neighbor pixel values).

In some embodiments, where the size of a neighborhood and/or kernel isknown, further efficiency may be achieved by omitting counter E. In thisregard, the total number of counts may be known based on the number ofpixels known to be in the neighborhood. In addition, it may be assumedthat any comparisons that do not result in counter E or counter G beingadjusted will correspond to those comparisons where pixels have equalvalues. Therefore, the value that would have been held by counter F maybe determined from counters E and G (e.g., (number of neighborhoodpixels)−counter E value−counter G value=counter F value).

In some embodiments, only a single counter may be used. In this regard,a single counter may be selectively adjusted in a first manner (e.g.,incremented or decremented) for each comparison where the selected pixelhas a greater value than a neighborhood pixel, selectively adjusted in asecond manner (e.g., decremented or incremented) for each comparisonwhere the selected pixel has a lesser value than a neighborhood pixel,and not adjusted (e.g., retaining its existing value) for eachcomparison where the selected pixel has an equal (e.g., exactly equal orsubstantially equal) value as a neighborhood pixel. Thus, the value ofthe single counter may indicate relative numbers of compared pixels thatare greater than or less than the selected pixel (e.g., after theselected pixel has been compared with all of its correspondingneighborhood pixels).

A NUC term for the selected pixel may be updated (e.g., incremented,decremented, or remain the same) based on the value of the singlecounter. For example, in some embodiments, if the single counterexhibits a baseline value (e.g., zero or other number) after comparisonsare performed, then the NUC term may remain the same. In someembodiments, if the single counter is greater or less than the baselinevalue, the NUC term may be selectively incremented or decremented asappropriate to reduce the overall differences between the selected pixeland the its corresponding neighborhood pixels. In some embodiments, theupdating of the NUC term may be conditioned on the single counter havinga value that differs from the baseline value by at least a thresholdamount to prevent undue skewing of the NUC term based on limited numbersof neighborhood pixels having different values from the selected pixel.

Various aspects of these techniques are further explained with regard toFIGS. 23B-E. In this regard, FIG. 23B is a flowchart illustrating amethod 3100 for noise filtering an infrared image, in accordance with anembodiment of the disclosure. Although particular components of system2100 are referenced in relation to particular blocks of FIG. 23B, thevarious operations described with regard to FIG. 23B may be performed byany appropriate components, such as image capture component 2130,processing component, 2110, noise filtering module 2112, memorycomponent 2120, control component 2140, and/or others. In someembodiments, the operations of FIG. 23B may be performed, for example,in place of blocks 565-573 of FIGS. 5 and 8.

In block 3110, an image frame (e.g., infrared image 3000) is received.For example, as discussed, infrared image 3000 may be an intentionallyblurred image frame provided by block 555 and/or 560.

In block 3120, noise filtering module 2112 selects a pixel of infraredimage 3000 for which a NUC term will be determined. For example, in someembodiments, the selected pixel may be pixel 3040, 3050, or 3060.However, any pixel of infrared image 3000 may be selected. In someembodiments, block 3120 may also include resetting counters E, F, and Gto zero or another appropriate default value.

In block 3130, noise filtering module 2112 selects a neighborhood (e.g.,a pixel neighborhood) associated with the selected pixel. As discussed,in some embodiments, the neighborhood may correspond to pixels within aselected distance of the selected pixel. In the case of selected pixel3040, a kernel of 5 corresponds to a neighborhood 3042 (e.g., including24 neighborhood pixels surrounding selected pixel 3040). In the case ofselected pixel 3050, a kernel of 5 corresponds to a neighborhood 3052(e.g., including 24 neighborhood pixels surrounding selected pixel3050). In the case of selected pixel 3060, a kernel of 5 corresponds toa neighborhood 3062 (e.g., including 24 neighborhood pixels surroundingselected pixel 3060). As discussed, larger and smaller kernel sizes arealso contemplated.

In blocks 3140 and 3150, noise filtering module 2112 compares theselected pixel to its neighborhood pixels and adjusts counters E, F, andG based on the comparisons performed in block 3140. Blocks 3140 and 3150may be performed in any desired combination such that counters E, F, andG may be updated after each comparison and/or after all comparisons havebeen performed.

In the case of selected pixel 3040, FIG. 23C shows the adjusted valuesof counters E, F, and G represented by a histogram 3200 after selectedpixel 3040 has been compared to the pixels of neighborhood 3042.Neighborhood 3042 includes 4 pixels having higher values, 17 pixelshaving equal values, and 3 pixels having lower values than selectedpixel 3040. Accordingly, counters E, F, and G may be adjusted to thevalues shown in FIG. 23C.

In the case of selected pixel 3050, FIG. 23D shows the adjusted valuesof counters E, F, and G represented by a histogram 3250 after selectedpixel 3050 has been compared to the pixels of neighborhood 3052.Neighborhood 3052 includes 0 pixels having higher values, 6 pixelshaving equal values, and 18 pixels having lower values than selectedpixel 3050. Accordingly, counters E, F, and G may be adjusted to thevalues shown in FIG. 23D.

In the case of selected pixel 3060, FIG. 23E shows the adjusted valuesof counters E, F, and G represented by a histogram 3290 after selectedpixel 3060 has been compared to the pixels of neighborhood 3062.Neighborhood 3062 includes 19 pixels having higher values, 5 pixelshaving equal values, and 0 pixels having lower values than selectedpixel 3060. Accordingly, counters E, F, and G may be adjusted to thevalues shown in FIG. 23E.

In block 3160, the NUC term for the selected pixel is updated (e.g.,selectively incremented, decremented, or remain the same) based on thevalues of counters E, F, and G. Such updating may be performed inaccordance with any appropriate calculation using the values of countersE, F, and G.

For example, in the case of selected pixel 3040, counter F in FIG. 23Cindicates that most neighborhood pixels (e.g., 17 neighborhood pixels)have values equal to selected pixel 3040, while counters E and Gindicate that smaller numbers of neighborhood pixels have values greaterthan (e.g., 4 neighborhood pixels) or less than (e.g., 3 neighborhoodpixels) selected pixel 3040. Moreover, the number of neighborhood pixelshaving values greater than and less than selected pixel 3040 are similar(e.g., 4 and 3 neighborhood pixels, respectively). Accordingly, in thiscase, noise filtering module 2112 may choose to keep the NUC term forselected pixel 3040 the same (e.g., unchanged) since a further offset ofselected pixel 3040 would likely introduce additional non-uniformityinto infrared image 3000.

In the case of selected pixel 3050, counter G in FIG. 23D indicates thatmost neighborhood pixels (e.g., 18 neighborhood pixels) have values lessthan selected pixel 3050, while counter F indicates that a smallernumber of neighborhood pixels (e.g., 6 neighborhood pixels) have valuesequal to selected pixel 3050, and counter E indicates that noneighborhood pixels (e.g., 0 neighborhood pixels) have values greaterthan selected pixel 3050. These counter values suggest that selectedpixel 3050 is exhibiting FPN that appears darker than most neighborhoodpixels. Accordingly, in this case, noise filtering module 2112 maychoose to decrement the NUC term for selected pixel 3050 (e.g., tolighten selected pixel 3050) such that it exhibits more uniformity withthe large numbers of neighborhood pixels having lower values.

In the case of selected pixel 3060, counter E in FIG. 23E indicates thatmost neighborhood pixels (e.g., 19 neighborhood pixels) have valuesgreater than selected pixel 3060, while counter F indicates that asmaller number of neighborhood pixels (e.g., 5 neighborhood pixels) havevalues equal to selected pixel 3060, and counter G indicates that noneighborhood pixels (e.g., 0 neighborhood pixels) have values less thanselected pixel 3060. These counter values suggest that selected pixel3060 is exhibiting FPN that appears lighter than most neighborhoodpixels. Accordingly, in this case, noise filtering module 2112 maychoose to increment the NUC term for selected pixel 3060 (e.g., todarken selected pixel 3060) such that it exhibits more uniformity withthe large numbers of neighborhood pixels having higher values.

In block 3160, changes to the NUC term for the selected pixel may bemade incrementally. For example, in some embodiments, the NUC term maybe incremented or decremented by a small amount (e.g., only one orseveral digital counts in some embodiments) in block 3160. Suchincremental changes can prevent large rapid changes in NUC terms thatmay inadvertently introduce undesirable non-uniformities in infraredimage 3000. The process of FIG. 23B may be repeated during eachiteration of FIGS. 5 and 8 (e.g., in place of blocks 565 and/or 570).Therefore, if large changes in the NUC term are required, then the NUCterm may be repeatedly incremented and/or decremented during eachiteration until the NUC value stabilizes (e.g., stays substantially thesame during further iterations). In some embodiments, the block 3160 mayfurther include weighting the updated NUC term based on local gradientsand/or temporal damping as described herein.

At block 3170, if additional pixels of infrared image 3000 remain to beselected, then the process returns to block 3120 wherein blocks3120-3170 are repeated to update the NUC term for another selectedpixel. In this regard, blocks 3120-3170 may be iterated at least oncefor each pixel of infrared image 3000 to update the NUC term for eachpixel (e.g., each pixel of infrared image 3000 may be selected and itscorresponding NUC term may be updated during a corresponding iterationof blocks 3120-3170).

At block 3180, after NUC terms have been updated for all pixels ofinfrared image 3000, the process continues to block 575 of FIGS. 5 and8. Operations of one or more of blocks 565-573 may also be performed inaddition to the process of FIG. 23B.

The process of FIG. 23B may be repeated for each intentionally blurredimage frame provided by block 555 and/or 560. In some embodiments, eachnew image frame received at block 3110 may not differ substantially fromother recently received image frames (e.g., in previous iterations ofthe process of FIG. 23B). This may be due to, for example, asubstantially static scene 2170, a slowing changing scene 2170, temporalfiltering of infrared images, and/or other reasons. In these cases, theaccuracy of NUC terms determined by FIG. 23B may improve as they areselectively incremented, decremented, or remain unchanged in eachiteration of FIG. 23B. As a result, in some embodiments, many of the NUCterms may eventually reach a substantially steady state in which theyremain relatively unchanged after a sufficient number of iterations ofFIG. 23B, and while the image frames do not substantially change.

Other embodiments are also contemplated. For example, block 3160 may berepeated multiple times to update one or more NUC terms using the sameinfrared image for each update. In this regard, after a NUC term isupdated in block 3160 or after multiple NUC terms are updated inadditional iterations of block 3160, the process of FIG. 23B may firstapply the one or more updated NUC terms (e.g., also in block 3160) tothe same infrared image used to determine the updated NUC terms andreturn to block 3120 to iteratively update one or more NUC terms usingthe same infrared image in such embodiments. Such an approach may beused, for example, in offline (non-realtime) processing and/or inrealtime implementations with sufficient processing capabilities.

Any of the various techniques described with regard to FIGS. 23A-E maybe combined where appropriate with the other techniques describedherein. For example, some or all portions of the various techniquesdescribed herein may be combined as desired to perform noise filtering.

Imaging systems are used to monitor almost all aspects of public life.Visible spectrum images of common public areas, such as seaways, roads,subways, parks, buildings, and building interiors, can be used insupport of a number of general security and safety organizations andapplications. Visible spectrum monitoring, however, is generally limitedto areas or scenes that are visibly illuminated, such as by the sun orby artificial visible spectrum lighting, for example, and to scenes thatare not otherwise obscured by environmental conditions. In accordancewith various embodiments of the present disclosure, infrared monitoringcan be used to supplement visible spectrum monitoring when the visiblespectrum monitoring is not providing sufficient information to monitor ascene according to a particular application need.

Imaging systems including infrared imaging modules, such as thosedescribed herein, can be used to extend the useful temporal range of amonitoring system to when a scene is not visibly illuminated, such as inlow light conditions, for example, or when visible spectrum details of ascene or objects within a scene are otherwise obscured. In particular,imaging systems including various embodiments of infrared imagingmodules 100 described herein have a number of advantages overconventional monitoring systems.

For example, infrared imaging modules 100 may be configured to monitortemperatures and conditions of scenes in relatively high detail and withrelatively high accuracy at or near real-time without the scenesnecessarily being illuminated sufficiently for simultaneous imaging byvisible spectrum imaging modules. This allows imaging systems to providedetailed and recognizable images, including streams of images (e.g.,video) of a scene regardless of whether current environmental conditionsallow visible spectrum imaging of the scene.

In some embodiments, infrared imaging modules 100 may be configured toproduce infrared images that can be combined with visible spectrumimages captured at a different time and produce high resolution, highcontrast, and/or targeted contrast combined images of a scene, forexample, that include highly accurate radiometric data (e.g., infraredinformation) corresponding to one or more objects in the scene. Forexample, imaging systems including infrared imaging module 100 can beconfigured to detect thermal excursions (e.g., abnormal temperatures),multiple types of gases (e.g., carbon monoxide, methane, fuel exhaustfumes, and/or other gasses or gas-like atomized liquids),density/partial density of gasses, and fluid leaks, for example, and cando so without being subject to the types of thermal or other sensor lagpresent in conventional sensors. Moreover, imaging systems includinginfrared imaging modules 100 can be configured to record any of theabove over time and detect minute changes in detected infraredemissions, temperatures, or related scene conditions.

In additional embodiment, infrared imaging modules 100 may be configuredto produce infrared images that can be combined with visible spectrumimages captured at substantially the same time and/or at different timesand produce high resolution, high contrast, and/or targeted contrastcombined images of a scene. In some embodiments, infrared images andvisible spectrum images may be combined using triple fusion processingoperations, for example, which may include selectable aspects ofnon-uniformity correction processing, true color processing, and highcontrast processing, as described herein. In such embodiments, theselectable aspects of the various processing operations may bedetermined by user input, threshold values, control parameters, defaultparameters, and/or other operating parameters of an imaging system. Forexample, a user may select and/or refine each individual relativecontribution of a non-uniformity correction, true color processedimages, and/or high contrast processed images, to combined imagesdisplayed to the user. The combined images may include aspects of allthree processing operations that can be adjusted in real-timeprogrammatically and/or by a user utilizing a suitable user interface.

In some embodiments, various image analytics and processing may beperformed according to a specific mode or context associated with anapplication, a scene, a condition of a scene, an imaging systemconfiguration, a user input, an operating parameter of an imagingsystem, and/or other logistical concerns. For example, in the overallcontext of maritime imaging, such modes may include a night dockingmode, a man overboard mode, a night cruising mode, a day cruising mode,a hazy conditions mode, a shoreline mode, a night-time display mode, ablending mode, a visible-only mode, an infrared-only mode, and/or othermodes, such as any of the modes described and/or provided in U.S. patentapplication Ser. No. 12/477,828. Types of analytics and processing mayinclude high and low pass filtering, histogram equalization, linearscaling, horizon detection, linear mapping, arithmetic image componentcombining, and other analytics and processing described in U.S. patentapplication Ser. No. 12/477,828, and/or U.S. patent application Ser. No.13/437,645.

Referring now to FIG. 24, FIG. 24 shows a block diagram of imagingsystem 4000 adapted to image scene 4030 in accordance with an embodimentof the disclosure. System 4000 may include one or more imaging modules,such as visible spectrum imaging module 4002 a and infrared imagingmodule 4002 b, processor 4010, memory 4012, communication module 4014,display 4016, and other components 4018. Where appropriate, elements ofsystem 4000 may be implemented in the same or similar manner as otherdevices and systems described herein and may be configured to performvarious NUC processes and other processes as described herein.

As shown in FIG. 24, scene 4030 (e.g., illustrated as a top plan view)may include various predominately stationary elements, such as building4032, windows 4034, and sidewalk 4036, and may also include variouspredominately transitory elements, such as vehicle 4040, cart 4042, andpedestrians 4050. Building 4032, windows 4034, sidewalk 4036, vehicle4040, cart 4042, and pedestrians 4050 may be imaged by visible spectrumimaging module 4002 a, for example, whenever scene 4030 is visiblyilluminated by ambient light (e.g., daylight) or by an artificialvisible spectrum light source, for example, as long as those elements ofscene 4030 are not otherwise obscured by smoke, fog, or otherenvironmental conditions. Building 4032, windows 4034, sidewalk 4036,vehicle 4040, cart 4042, and pedestrians 4050 may be imaged by infraredimaging module 4002 b to provide real-time imaging and/or low-lightimaging of scene 4030 when scene 4030 is not visibly illuminated (e.g.,by visible spectrum light), for example.

In some embodiments, imaging system 4000 can be configured to combinevisible spectrum images from visible spectrum imaging module 4002 acaptured at a first time (e.g., when scene 4030 is visibly illuminated),for example, with infrared images from infrared imaging module 4002 bcaptured at a second time (e.g., when scene 4030 is not visiblyilluminated), for instance, in order to generate combined imagesincluding radiometric data and/or other infrared characteristicscorresponding to scene 4030 but with significantly more object detailand/or contrast than typically provided by the infrared or visiblespectrum images alone. In other embodiments, the combined images caninclude radiometric data corresponding to one or more objects withinscene 4030, for example, and visible spectrum characteristics, such as avisible spectrum color of the objects (e.g., for predominantlystationary objects), for example. In some embodiments, both the infraredimages and the combined images can be substantially real time images orvideo of scene 4030. In other embodiments, combined images of scene 4030can be generated substantially later in time than when correspondinginfrared and/or visible spectrum images have been captured, for example,using stored infrared and/or visible spectrum images and/or video. Instill further embodiments, combined images may include visible spectrumimages of scene 4030 captured before or after corresponding infraredimages have been captured.

In each embodiment, visible spectrum images including predominatelystationary elements of scene 4030, such as building 4032, windows 4034,and sidewalk 4036, can be processed to provide visible spectrumcharacteristics that, when combined with infrared images, allow easierrecognition and/or interpretation of the combined images. In someembodiments, the easier recognition and/or interpretation extends toboth the predominately stationary elements and one or more transitoryelements (e.g., vehicle 4040, cart 4042, and pedestrians 4050) in scene4030.

For example, a visible spectrum image of building 4032, windows 4034,and sidewalk 4036 captured by visible spectrum imaging module 4002 a ata first time while scene 4030 is visibly illuminated can be combinedwith a relatively low resolution and/or real time infrared imagecaptured by infrared imaging module 4002 b at a second time (e.g., whileobjects within scene 4030 are obscured in the visible spectrum or notvisibly illuminated) to generate a combined image with sufficientradiometric data, detail, and contrast to allow a user viewing thecombined image (e.g., on display 4016) to more easily detect and/orrecognize each of vehicle 4040, cart 4042, and pedestrians 4050.

In further embodiments, such a combined image may allow a user or amonitoring system to more easily detect and/or recognize pedestrian 4050situated behind vehicle 4040 with respect to infrared imaging module4002 b. For example, visible spectrum characteristics derived from aprior visible spectrum image of scene 4030 may be used to add sufficientcontrast and detail to a combined image including a radiometriccomponent (e.g., radiometric data) of a real time infrared image so thata user and/or monitoring system can detect and recognize at least one ofa spatial distinction, a temperature difference, and a gas-typedifference, between one or more of an exhalation of pedestrian 4050, anexhaust fume from vehicle 4040, and an ambient temperature of building4032, windows 4034, or sidewalk 4036.

Visible spectrum imaging module 4002 a may be implemented as any type ofvisible spectrum camera or imaging device capable of imaging at least aportion of scene 4030 in the visible spectrum. In some embodiments,visible spectrum imaging module 4002 a may be a small form factorvisible spectrum camera or imaging device, and visible spectrum imagingmodule 4002 a may be implemented similarly to various embodiments of aninfrared imaging module disclosed herein, but with one or more sensorsadapted to capture radiation in the visible spectrum. For example, insome embodiments, imaging module 4002 a may be implemented with acharge-coupled device (CCD) sensor, an electron multiplying CCD (EMCCD)sensor, a complementary metal-oxide-semiconductor (CMOS) sensor, ascientific CMOS (sCMOS) sensor, or other sensors.

Visible spectrum imaging module 4002 a may include an FPA of visiblespectrum sensors, for example, and may be configured to capture,process, and/or manage visible spectrum images of scene 4030. Visiblespectrum imaging module 4002 a may be configured to store and/ortransmit captured visible spectrum images according to a variety ofdifferent color spaces/formats, such as YCbCr, RGB, and YUV, forexample, and individual visible spectrum images may be color correctedand/or calibrated according to their designated color space and/orparticular characteristics of visible spectrum imaging module 4002 a.

In some embodiments, infrared imaging module 4002 b may be a small formfactor infrared camera or imaging device implemented in accordance withvarious embodiments disclosed herein. For example, infrared imagingmodule 4002 b may include an FPA implemented in accordance with variousembodiments disclosed herein or otherwise where appropriate. Infraredimaging module 4002 b may be configured to capture, process, and/ormanage infrared images, including thermal images, of at least portionsof scene 4030. For example, FIG. 29 illustrates an unprocessed infraredimage 4500 captured by an infrared imaging module in accordance with anembodiment of the disclosure. FIG. 35 illustrates a picture-in-picturecombined image 5100 including a low resolution infrared image 5102 of ascene captured by an infrared imaging module in accordance with anotherembodiment of the disclosure.

Infrared imaging module 4002 b may be configured to store and/ortransmit captured infrared images according to a variety of differentcolor spaces/formats, such as YCbCr, RGB, and YUV, for example, whereradiometric data may be encoded into one or more components of aspecified color space/format. In some embodiments, a common color spacemay be used for storing and/or transmitting infrared images and visiblespectrum images.

Imaging modules 4002 a-b may be mounted so that at least a portion ofscene 4030 is within a shared field of view (FOV) of imaging modules4002 a-b. In various embodiments, imaging modules 4002 a-b may includerespective optical elements 4004 a-b (e.g., visible spectrum and/orinfrared transmissive lenses, prisms, reflective mirrors, fiber optics)that guide visible spectrum and/or infrared radiation from scene 4030 tosensors (e.g., FPAs) of imaging modules 4002 a-b. Such optical elementsmay be used when mounting an imaging module at a particular FOV-definedlocation is otherwise difficult or impossible. For example, a flexiblefiber-optic cable may be used to route visible spectrum and/or infraredradiation from within a sealed building compartment, such as a bankvault or an air-handling vent, to an imaging module mounted outside thesealed building compartment. Such optical elements may also be used tosuitably define or alter an FOV of an imaging module. A switchable FOV(e.g., selectable by a corresponding imaging module and or processor4010) may optionally be provided to provide alternating far-away andclose-up views of a portion scene 4030, for example, or to providefocused and de-focused views of scene 4030.

In some embodiments, one or more of visible spectrum imaging module 4002a and/or infrared imaging module 4002 b may be configured to be panned,tilted, and/or zoomed to view the surrounding environment in any desireddirection (e.g., any desired portion of scene 4030 and/or other portionsof the environment). For example, in some embodiments, of visiblespectrum imaging module 4002 a and/or infrared imaging module 4002 b maybe pan-tilt-zoom (PTZ) cameras that may be remotely controlled, forexample, from appropriate components of imaging system 4000.

In some embodiments, it is contemplated that at least one of imagingmodules 4002 a/4002 b may capture an image of a relatively large portionof scene 4030, and at least another one of imaging modules 4002 a/4002 bmay subsequently capture another image of a smaller subset of the scene4030 (e.g., to provide an image of an area of interest of scene 4030).For example, it is contemplated that a visible spectrum or infraredimage of a large portion of scene 4030 may be captured, and that aninfrared or visible spectrum image of a subset of scene 4030 may besubsequently captured and overlaid, blended, and/or otherwise combinedwith the previous image to permit a user to selectively view visiblespectrum and/or infrared image portions of the subset of scene 4030 asmay be desired.

As illustrated by the embodiment of system 4000 shown in FIG. 24,imaging modules 4002 a-b may be implemented such that an optical axis ofvisible spectrum imaging module 4002 a is parallel to and a distance “d”from an optical axis of infrared imaging module 4002 b. Furthermore, theimaging modules 4002 a and 4002 b may have differing FOVs, designated inFIG. 24 by respective angles α and β, which may be different angles(e.g., as illustrated in FIG. 24) or substantially the same. In suchembodiments, one or more of imaging modules 4002 a-b and/or processor4010 may be configured to correct for differing FOVs and/or parallaxresulting from non-zero “d” and/or (α−β) when generating combinedimages, as described herein.

In some embodiments, system 4000 may include multiple imaging modules(e.g., two or more) of both types, where the group of imaging modulesmay have a variety of non-parallel optical axes and differing FOVs ofscene 4030. In such embodiments, one or more of the constituent imagingmodules and/or processor 4010 may be configured to correct for all orsome subset of non-aligned optics when generating combined imagesincluding visible spectrum characteristics of scene 4030 derived fromcaptured visible spectrum images and infrared characteristics of scene4030 derived from captured infrared images (e.g., a radiometriccomponent of the infrared images).

In other embodiments, the imaging modules may be implemented to share asingle selectable set of optical elements (e.g., with selectable visiblespectrum and infrared optical elements, depending on a type of imagebeing captured), for example, so that the imaging modules have the samefield of view and the same optical axis. In such embodiments, FOV and/orparallax corrections may not be performed.

Infrared images captured, stored and/or transmitted by imaging module4002 b may be stored, transmitted and/or processed by one or more ofimaging module 4002 b and processor 4010 in a variety of color spaces,for example, such as YCbCr, RGB, YUV, and other known or proprietarycolor spaces. In some embodiments, radiometric data corresponding to ameasurement of infrared emissions impinging upon an infrared sensor orFPA of infrared sensors may be encoded into one or more components of aninfrared image. For example, where a designated color space for aninfrared image is YCbCr, radiometric data captured by infrared imagingmodule 4002 b may be encoded into a luminosity component (e.g., Y) ofthe infrared image. In a related embodiment, a corresponding chrominancecomponent (e.g., Cr and Cb) of the infrared image may be eliminated,truncated, and/or unused, for example, or may be set to a particularknown value, such as grey or a combination of one or more primarycolors.

In other embodiments, radiometric data may be encoded into a chrominancecomponent (e.g., Cr and Cb) of a corresponding infrared image while aluminance component (e.g., Y) is set to a particular known value, suchas a mid-level value. For example, a range of radiometric data may beencoded into a range of a single primary color, for instance, or may beencoded into a range of a combination of primary colors. In oneembodiment, encoding radiometric data into one or more primary colorsmay include applying a pseudo-color palette to the radiometric componentof an infrared image.

In further embodiments, radiometric data may be encoded into bothluminance and chrominance components of an infrared image (e.g., Y andCr and Cb). For example, infrared imaging module 4002 b may be adaptedto sense infrared radiation across a particular band of infraredfrequencies. A luminance component may include radiometric datacorresponding to intensity of infrared radiation, and a chrominancecomponent may include radiometric data corresponding to what frequencyof infrared radiation is being sensed (e.g., according to a pseudo-colorpalette). In such an embodiment, a radiometric component of theresulting infrared image may include both luminance and chrominancecomponents of the infrared image.

In still further embodiments, infrared images captured by infraredimaging module 4002 b may be stored according to a module-specific colorspace, for example, and be stored as raw radiometric data (e.g.,uncompressed) for each pixel of infrared imaging module 4002 b. In someembodiments, a radiometric component of the resulting infrared image mayinclude the raw radiometric data, and one or more of imaging modules4002 a-b and/or processor 4010 may be configured process the rawradiometric data to generate combined images including infraredcharacteristics of scene 4030.

Infrared images captured, processed, and otherwise managed by infraredimaging module 4002 b may be radiometrically normalized infrared images(e.g., thermal images). Pixels that make up a captured image may containcalibrated thermal data (e.g., absolute temperatures). As discussedabove in connection with infrared imaging module 100 of FIG. 1, infraredimaging module 4002 b and/or associated components may be calibratedusing appropriate techniques so that images captured by the infraredimaging module are properly calibrated infrared images. In someembodiments, appropriate calibration processes may be performedperiodically by infrared imaging module 4002 b and/or processor 4010 sothat the infrared imaging module and its captured infrared imagesmaintain accurate calibration. In other embodiments, infrared imagingmodule 4002 b and/or processor 4010 may be configured to perform otherprocesses to emphasize a desired range or interval of radiometric data,for example, and allocate a dynamic range of one or more components of aresulting infrared image according to the desired range of radiometricdata. Thus, a radiometric component of an infrared image may includecalibrated radiometric data, un-calibrated radiometric data, and/oradjusted radiometric data.

Processor 4010 may be implemented as any appropriate processing devicedescribed herein. In some embodiments, processor 4010 may be part of orimplemented with other conventional processors and control electronicsof a monitoring system monitoring scene 4030. For example, a monitoringsystem for scene 4030 may include one or more processors or controlelectronics for controlling alarms, processing image or video data,and/or notifying various users, any of which may be used to implementall or part of processor 4010. In other embodiments, processor 4010 mayinterface and communicate with such other control electronics andprocessors as well as any monitoring system components associated withsuch processors. In some embodiments, processor 4010 may be configuredto control, monitor, and or communicate with lights, animated signs, orsirens in or near scene 4030, for example, and in some embodiments, doso according to a schedule set by a user, a technician, or by default ata factory. Such schedule may determine whether a particular notificationor type of notification is provided to a user, for example, or todetermine when one or more monitoring system components are enabled.

Processor 4010 may be configured to interface and communicate with othercomponents of system 4000 to perform methods and processes describedherein, including to provide control signals to one or more componentsof a monitoring system monitoring scene 4030. Processor 4010 may beconfigured to receive visible spectrum and infrared (e.g., thermal)images of at least a portion of scene 4030 captured by imaging modules4002 a-b at first and second times (e.g., while visibly illuminated andwhile not visibly illuminated), perform image processing operations asfurther described herein, and generate combined images from the capturedimages to, for example, provide high resolution, high contrast, ortargeted contrast combined images of portions of and/or objects in scene4030. Processor 4010 may also be configured to compile, analyze, orotherwise process visible spectrum images, infrared images, and contextdata (e.g., time, date, environmental conditions) to generate monitoringinformation about scene 4030, such as monitoring information aboutdetected objects in scene 4030.

For example, processor 4010 may determine, from combined imagesincluding radiometric data from calibrated infrared images provided byinfrared imaging module 4002 b, aggregate temperature of an object orportion of an object in scene 4030. Processor 4010 may generatemonitoring information that includes, for example, a temperature readingbased on the determined temperature. Processor 4010 may furtherdetermine whether the temperature of an object is within a typicaloperating temperature range, and generate monitoring information thatincludes a notification or alarm indicating the temperature is outside atypical range.

In another example, processor 4010 may perform various image processingoperations and image analytics on visible spectrum, infrared, and/orcombined images of an object in scene 4030 to obtain temperaturedistribution and variance profiles of the object. Processor 4010 maycorrelate and/or match the obtained profiles to those of abnormalconditions to detect, for example, an overflowing manhole cover, anabundance of methane or other gas build-up near an object in scene 4030,a leaking fire hydrant, a running vehicle (e.g., exhaust fumes), orother conditions of scene 4030.

In yet another example, processor 4010 may perform various imageprocessing operations and image analytics on visible spectrum, infrared,and/or combined images of scene 4030 to detect transitory objectsentering scene 4030. Based on the detection, processor 4010 may generatemonitoring information that includes an alarm or other visual or audiblenotifications that indicate arrival of a transitory object.

In some embodiments, processor 4010 may be configured to convert visiblespectrum, infrared, and/or combined images of portions of power system4030 into user-viewable images (e.g., thermograms) using appropriatemethods and algorithms. For example, thermographic data contained ininfrared and/or combined images may be converted into gray-scaled orcolor-scaled pixels to construct images that can be viewed on a display.Such conversion may include adjusting a dynamic range of one or morecomponents of the combined images to match a dynamic range of display4016, for example, to emphasize a particular radiometric interval,and/or to increase a perceived contrast of user-viewable images.User-viewable images may optionally include a legend or scale thatindicates the approximate temperature of a corresponding pixel colorand/or intensity. Such user-viewable images, if presented on a display(e.g., display 4016), may be used to confirm or better understandconditions of scene 4030 detected by system 4000. Monitoring informationgenerated by processor 4010 may include such user-viewable images.

Memory 4012 may include one or more memory devices (e.g., memorycomponents) to store data and information, including visible spectrum,infrared, and/or combined images, context data, and monitoringinformation. The memory devices may include various types of memory forimage and other information storage including volatile and non-volatilememory devices, such as RAM (Random Access Memory), ROM (Read-OnlyMemory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory,a disk drive, and other types of memory described herein. In oneembodiment, images, context data, and monitoring information stored inthe memory devices may be retrieved (e.g., by a user) for purposes ofreviewing and further diagnosing a detected condition of scene 4030 orrefining a method of generating combined images from captured visiblespectrum and infrared images. In another embodiment, memory 4012 mayinclude a portable memory device that can be removed from system 4000and used to convey stored data to other systems, including monitoringsystems, for further processing and inspection. In some embodiments,processor 4010 may be configured to execute software instructions storedon memory 4012 and/or machine readable medium 193 to perform variousmethods, processes, or operations in the manner described herein.

Display 4016 may be configured to present, indicate, or otherwise conveycombined images and/or monitoring information generated by processor4010. In one embodiment, display 4016 may be implemented with variouslighted icons, symbols, indicators, and/or gauges which may be similarto conventional indicators, gauges, and warning lights of a conventionalmonitoring system. The lighted icons, symbols, and/or indicators mayindicate one or more notifications or alarms associated with thecombined images and/or monitoring information. The lighted icons,symbols, or indicators may also be complemented with an alpha-numericdisplay panel (e.g., a segmented LED panel) to display letters andnumbers representing other monitoring information, such as a temperaturereading, a description or classification of detected conditions, etc.

In other embodiments, display 4016 may be implemented with an electronicdisplay screen, such as a liquid crystal display (LCD), a cathode raytube (CRT), or various other types of generally known video displays andmonitors, including touch-sensitive displays. Display 4016 may besuitable for presenting user-viewable visible spectrum, infrared, and/orcombined images retrieved and/or generated by processor 4010 from imagescaptured by imaging modules 4002 a-b. It is contemplated thatconventional monitoring system display screens may be utilized asdisplay 4016.

Communication module 4014 may be configured to facilitate communicationand interfacing between various components of system 4000. For example,elements such as imaging modules 4002 a-b, display 4016, and/or othercomponents 4018 may transmit and receive data to and from processor 4010through communication module 4014, which may manage wired and/orwireless connections (e.g., through proprietary RF links, proprietaryinfrared links, and/or standard wireless communication protocols such asIEEE 802.11 WiFi standards and Bluetooth™) between the variouscomponents. Such wireless connections may allow imaging modules 4002 a-bto be mounted where it would not be convenient to provide wiredconnections, for example.

Communication module 4014 may be further configured to allow componentsof system 4000 to communicate and interface with other components of amonitoring system monitoring scene 4030. For example, processor 4010 maycommunicate, via communication module 4014, with a motion detector,smoke detector, and other existing sensors and electronic components. Inthis regard, communication module 4014 may support various interfaces,protocols, and standards for networking, such as the controller areanetwork (CAN) bus, the local interconnect network (LIN) bus, the mediaoriented systems transport (MOST) network, or the ISO 11738 (or ISO bus)standard. Furthermore, communication module 4014 may be configured tosend control signals generated by processor 4010 using these interfacesand protocols.

In some embodiments, system 4000 may include a number of communicationmodules 4014 adapted for various applications of system 4000 withrespect to various types of scenes. In other embodiments, communicationmodule 4014 may be integrated into or implemented as part of variousother components of system 4000. For example, imaging modules 4002 a-b,processor 4010, and display 4016 may each comprise a subcomponent thatmay be configured to perform the operations of communication module4014, and may communicate with one another via wired and/or wirelessconnections without a separate communication module 4014.

Other components 4018 may include, in some embodiments, other sensorssuch as a temperature sensor (e.g., a thermocouple, an infraredthermometer), a moisture sensor, an electrical sensor (e.g., avolt/current/resistance meter), a pressure sensor (e.g., a barometer),and/or a visible spectrum light meter. Data from sensors such as atemperature, moisture, pressure, or light sensor may be utilized byprocessor 4010 to detect and potentially compensate for environmentalconditions (e.g., fog, smoke, or other low-light condition), and therebyobtain more accurate or more easily interpretable combined images andderived conditions of scene 4030.

Other components 4018 may also include any other device as may bebeneficial for various applications of system 4000. In some embodiments,other components 4018 may include a chime, a speaker with associatedcircuitry for generating a tone, or other devices that may be used tosound an audible alarm or notification based on combined imagesgenerated by processor 4010. In further embodiments, other components4018 may include a user interface to accept user input of, for example,a desired method of generating combined images, a target contrast orcorresponding radiometric interval and/or dynamic range, a notificationsetting of system 4000, external sensor data, or context information.

In various embodiments, one or more components of system 4000 may becombined and/or implemented or not, depending on applicationrequirements. For example, processor 4010 may be combined with any ofimaging modules 4002 a-b, memory 4012, display 4016, and/orcommunication module 4014. In another example, processor 4010 may becombined with any of imaging modules 4002 a-b with only certainoperations of processor 4010 performed by circuitry (e.g., a processor,logic device, microprocessor, microcontroller, etc.) within any of theinfrared imaging modules.

Thus, one or more components of system 4000 may be mounted in view ofscene 4030 to provide real-time and/or enhanced infrared monitoring ofscene 4030 in low light situations. For example, system 4000 may be usedto detect transitory objects, liquid leaks, gas build up, and abnormaltemperatures in scene 4030.

Turning to FIG. 25, FIG. 25 illustrates a flowchart of a process 4100 toenhance infrared imaging of a scene in accordance with an embodiment ofthe disclosure. For example, one or more portions of process 4100 may beperformed by processor 4010 and/or each of imaging modules 4002 a-b ofsystem 4000 and utilizing any of optical elements 4004 a-b, memory 4012,communication module 4014, display 4016, or other components 4018, whereeach of imaging modules 4002 a-b and/or optical elements 40104 a-b maybe mounted in view of at least a portion of scene 4030. In someembodiments, some elements of system 4000 may be mounted in adistributed manner (e.g., be placed in different areas inside or outsideof scene 4030) and be coupled wirelessly to each other using one or morecommunication modules 4014. In further embodiments, imaging modules 4002a-b may be situated out of view of scene 4030 but may receive views ofscene 4030 through optical elements 40104 a-b.

It should be appreciated that system 4000 and scene 4030 are identifiedonly for purposes of giving examples and that any other suitable systemmay include one or more components mounted in view of any other type ofscene and perform all or part of process 4100. It should also beappreciated that any step, sub-step, sub-process, or block of process4100 may be performed in an order or arrangement different from theembodiment illustrated by FIG. 25. For example, although process 4100describes visible spectrum images being captured before infrared imagesare captured, in other embodiments, visible spectrum images may becaptured after infrared images are captured.

In some embodiments, any portion of process 4100 may be implemented in aloop so as to continuously operate on a series of infrared and/orvisible spectrum images, such as a video of scene 4030. In otherembodiments, process 4100 may be implemented in a partial feedback loopincluding display of intermediary processing (e.g., after or whilereceiving infrared and/or visible spectrum images, performingpreprocessing operations, generating combined images, performing postprocessing operations, or performing other processing of process 4100)to a user, for example, and/or including receiving user input, such asuser input directed to any intermediary processing step.

At block 4102, system 4000 may receive (e.g., accept) user input. Forexample, display 4016 and/or other components 4018 may include a userinput device, such as a touch-sensitive screen, keyboard, mouse, dial,or joystick. Processor 4010 of system 4000 may be configured to promptfor user input, using display 4016 or an audible tone, for example, andreceive the user input from a user input device (e.g., one or more ofother components 4018) to determine a method of generating combinedimages, to select a radiometric interval, to input context and/or sensordata, to select a color or pseudo-color palette for one or more imagetypes, to select or refine a blending parameter, to select or refine acontrol parameter, to select or refine threshold values, or to determineother operating parameters of system 4000, as described herein. Forexample, system 4000 may prompt a user to select a blending or a highcontrast mode for generating combined images of scene 4030, and uponreceiving user input, system 4000 may proceed with a selected mode.

System 4000 may be configured to receive user input at any point duringprocess 4100. For example, embodiments of block 4102 may be placedbefore, after, or within any block of process 4100. In some embodiments,system 4000 may be configured to receive user input to select or refinea blending parameter, a control parameter, and/or other operatingparameter, for example, while system 4000 is performing an embodiment ofblock 4130 (e.g., generating combined images) that includes a feedbackloop. In such embodiments, the feedback loop may include displayingcombined images to a user (e.g., using display 4016) while receivinguser input selecting, refining, or adjusting a blending parameter and/ora method of generating combined images, for example. The user input maybe used to generate new and/or adjusted combined images that are thendisplayed to a user for evaluation, for example (e.g., an embodiment ofblock 4152). In further embodiments, a feedback loop may includereceiving additional user input to exit the feedback loop and continuewith process 4100, for example, or to re-enter process 4100 at any otherstep, sub-step, sub-process, or block of process 4100.

In other embodiments, user input may be used to control a pan, tilt, orzoom feature of one or more of imaging modules 4002 a-b. For example, afeedback loop may include displaying to a user a first image of scene4030 captured by one of imaging modules 4002 a-b according to a firstperspective, receiving user input to pan, tilt, and/or zoom the otherimaging module to a similar and/or further zoomed-in second perspectiveto highlight a portion-of-interest of scene 4030, and then displaying asecond image captured by the other imaging module and/or a combinedimage including aspects of the first and second perspectives, to theuser. In some embodiments, the combined image may be an overlay and/or ablending of the second image with the first image, generated accordingto processing operations described herein.

FIG. 35 illustrates a combined image 5100 that can be generatedaccording to such a feedback loop. For example, infrared image 5102 maybe captured according to a first perspective by infrared imaging module4002 b and displayed to a user. System 4000 may then receive user inputto pan, tilt, and/or zoom visible spectrum imaging module 4002 a to asecond perspective (e.g., illustrated by portion 5104) and capture avisible spectrum image according to the second perspective. System 4000may then display combined image 5100 including aspects of the firstperspective (e.g., infrared image 5102) and aspects of the secondperspective (e.g., the visible spectrum image corresponding to highspatial frequency content 5106, and/or high spatial frequency content5106) to the user. Thus, embodiments of system 4000 allow a user and/ormonitoring system to image/monitor different perspectives of scene 4030at the same time or at different times and then adjust one or moreperspectives of the imaging modules to image/monitorportions-of-interest of scene 1430.

At block 4104, system 4000 may determine one or more threshold valuesfor use in process 4100. For example, processor 4010 and/or imagingmodules 4002 a-b may be configured to determine threshold values fromuser input received in block 4102. In one embodiment, processor 4010 maybe configured to determine threshold values from images and/or imagedata captured by one or more modules of system 4000. In variousembodiments, processor 4010 may be configured to use such thresholdvalues to set, adjust, or refine one or more control parameters,blending parameters, or other operating parameters as described herein.For example, threshold values may be associated with one or moreprocessing operations, such as blocks 4120-4140 of FIG. 25, blocks4232-4238 of FIG. 26, and blocks 4320-4326 of FIG. 27, for example.

In one embodiment, threshold values may relate to a method forgenerating combined images, as described more fully below. Suchthreshold values may be used to determine a method for generatingcombined images, a blending parameter for generating blended image data,a limit or gain associated with de-noising an image, one or more controlparameters for determining relative contributions to a combined image,or to determine other aspects of generating combined images as describedherein. In some embodiments, threshold values may be used to determineaspects of processing steps on a pixel-by-pixel basis, for example, orfor regions of visible spectrum, infrared, and/or combined images. Inone embodiment, threshold values may be used to select defaultparameters (e.g., blending parameters, control parameters, and otheroperating parameters) for use with one or more processing operationsdescribed herein.

In similar fashion to block 4102, system 4000 may be configured todetermine threshold values at any point during process 4100. Forexample, embodiments of block 4104 may be placed before, after, orwithin any block of process 4100, including embodiments with an includedfeedback loop. In such embodiments, the feedback loop may includedisplaying combined images to a user (e.g., using display 4016) whiledetermining threshold values associated with selecting, refining, oradjusting a blending parameter, a control parameter, a method ofgenerating combined images, and/or other operating parameters, forexample. The determined threshold values may be used to generate newand/or adjusted combined images that are then displayed to a user forevaluation, for example (e.g., an embodiment of block 4152).

At block 4110, system 4000 may capture one or more visible spectrumimages. For example, processor 4010 and/or visible spectrum imagingmodule 4002 a may be configured to capture a visible spectrum image ofscene 4030 at a first time, such as while scene 4030 is visiblyilluminated. In one embodiment, processor 4010, visible spectrum imagingmodule 4002 a, and/or other components 4018 may be configured to detectcontext data, such as time of day and/or lighting or environmentalconditions, and determine an appropriate first time by determining thatthere is sufficient ambient light and environmental clarity to capture avisible spectrum image with enough detail and/or contrast to discernobjects or to generate a combined image with sufficient detail and/orcontrast for a particular application of system 4000, such as intrusionmonitoring or fire safety monitoring. In other embodiments, processor4010 and/or visible spectrum imaging module 4002 a may be configured tocapture visible spectrum images according to user input and/or aschedule. Visible spectrum imaging module 4002 a may be configured tocapture visible images in a variety of color spaces/formats, including araw or uncompressed format.

At block 4112, system 4000 may receive and/or store visible spectrumimages and associated context information. For example, processor 4010and/or visible spectrum imaging module 4002 a may be configured toreceive visible spectrum images of scene 4030 from a sensor portion ofvisible spectrum imaging module 4002 a, to receive context data fromother components 4018, and then to store the visible spectrum imageswith the context data in a memory portion of visible spectrum imagingmodule 4002 a and/or memory 4012.

Context data may include various properties and ambient conditionsassociated with an image of scene 4030, such as a timestamp, an ambienttemperature, an ambient barometric pressure, a detection of motion inscene 4030, an orientation of one or more of imaging modules 4002 a-b, aconfiguration of one or more of optical elements 4004 a-b, the timeelapsed since imaging has begun, and/or the identification of objectswithin scene 4030 and their coordinates in one or more of the visiblespectrum or infrared images.

Context data may guide how an image may be processed, analyzed, and/orused. For example, context data may reveal that an image has been takenwhile an ambient light level is high. Such information may indicate thata captured visible spectrum image may need additional exposurecorrection pre-processing. In this and various other ways, context datamay be utilized (e.g., by processor 4010) to determine an appropriateapplication of an associated image. Context data may also supply inputparameters for performing image analytics and processing as furtherdescribed in detail below. In different embodiments, context data may becollected, processed, or otherwise managed at a processor (e.g.,processor 4010) directly without being stored at a separate memory.

Visible spectrum images may be stored in a variety of colorspaces/formats that may or may not be the color space/format of thereceived visible spectrum images. For example, processor 4010 may beconfigured to receive visible spectrum images from visible spectrumimaging module 4002 a in an RGB color space, then convert and save thevisible spectrum images in a YCbCr color space. In other embodiments,processor 4010 and/or visible spectrum imaging module 4002 a may beconfigured to perform other image processing on received visiblespectrum images prior to storing the images, such as scaling, gaincorrection, color space matching, and other preprocessing operationsdescribed herein with respect to block 4120.

At block 4114, system 4000 may optionally be configured to wait a periodof time. For example, processor 4010 may be configured to wait untilscene 4030 is not visibly illuminated (e.g., in the visible spectrum),or until scene 4030 is obscured in the visible spectrum by environmentalconditions, for instance, before proceeding with process 4100. In otherembodiments, processor 4010 may be configured to wait a scheduled timeperiod or until a scheduled time before proceeding with process 4100.The time and/or time period may be adjustable depending on ambient lightlevels and/or environmental conditions, for example. In someembodiments, the period of time may be a substantial period of time,such as twelve hours, days, weeks, or other time period that isrelatively long compared to a typical time for motion of objects (e.g.,vehicles, pedestrians) within scene 4030.

At block 4116, system 4000 may capture one or more infrared images. Forexample, processor 4010 and/or infrared imaging module 4002 b may beconfigured to capture an infrared image of scene 4030 at a second time,such as while scene 4030 is not visibly illuminated, or after aparticular time period enforced in block 4114. Examples of unprocessedinfrared images captured by an infrared imaging module are provided inFIGS. 17 and 21.

In some embodiments, the second time may be substantially different fromthe first time referenced in block 4110, relative to the time typicallyneeded for a transient object to enter and leave scene 4030, forexample. Processor 4010 and/or infrared imaging module 4002 b may beconfigured to detect context data, such as time, date, and lightingconditions, and determine an appropriate second time by determining thatambient light levels are too low to capture a visible spectrum imagewith sufficient detail and/or contrast to discern objects in scene 4030according to a particular application of system 4000. In someembodiments, processor 4010 and/or infrared imaging module 4002 b may beconfigured to determine an appropriate second time by analyzing one ormore visible spectrum and/or infrared images captured by imaging modules4002 a-b. In other embodiments, processor 4010 and/or infrared imagingmodule 4002 b may be configured to capture infrared images according touser input and/or a schedule. Infrared imaging module 4002 b may beconfigured to capture infrared images in a variety of colorspaces/formats, including a raw or uncompressed format. Such images mayinclude radiometric data encoded into a radiometric component of theinfrared images.

At block 4118, system 4000 may receive and/or store infrared images andassociated context information. For example, processor 4010 and/orinfrared imaging module 4002 b may be configured to receive infraredimages of scene 4030 from a sensor portion of infrared imaging module4002 a, to receive context data from other components 4018, and then tostore the infrared images with the context data in a memory portion ofinfrared imaging module 4002 b and/or memory 4012. Context data mayinclude various properties and ambient conditions associated with animage, for example, and may guide how an image may be processed,analyzed, and/or used.

Infrared images may be stored in a variety of color spaces/formats thatmay or may not be the color space/format of the received infraredimages. For example, processor 4010 may be configured to receiveinfrared images from infrared imaging module 4002 b in a raw radiometricdata format, then convert and save the infrared images in a YCbCr colorspace. In some embodiments, radiometric data may be encoded entirelyinto a luminance (e.g., Y) component, a chrominance (e.g., Cr and Cb)component, or both the luminance and chrominance components of theinfrared images, for example. In other embodiments, processor 4010and/or infrared imaging module 4002 b may be configured to perform otherimage processing on received infrared images prior to storing theimages, such as scaling, gain correction, color space matching, andother preprocessing operations described herein with respect to block4120.

At block 4120, system 4000 may perform a variety of preprocessingoperations. For example, one or more of imaging modules 4002 a-b and/orprocessor 4010 may be configured to perform one or more preprocessingoperations on visible spectrum and/or infrared images of scene 4030captured by imaging modules 4002 a-b.

Preprocessing operations may include a variety of numerical, bit, and/orcombinatorial operations performed on all or a portion of an image, suchas on a component of an image, for example, or a selection of pixels ofan image, or on a selection or series of images. In one embodiment,processing operations may include operations for correcting fordiffering FOVs and/or parallax resulting from imaging modules 4002 a-bhaving different FOVs or non-co-linear optical axes. Such correctionsmay include image cropping, image morphing (e.g., mapping of pixel datato new positions in an image), spatial filtering, and resampling, forexample. In another embodiment, a resolution of the visible spectrumand/or infrared images may be scaled to approximate or match aresolution of a corresponding image (e.g., visible spectrum to infrared,or infrared to visible spectrum), a portion of an image (e.g., for apicture-in-picture (PIP) effect), a resolution of display 4016, or aresolution specified by a user, monitoring system, or particular imageprocessing step. Resolution scaling may include resampling (e.g.,up-sampling or down-sampling) an image, for example, or may includespatial filtering and/or cropping an image.

In another embodiment, preprocessing operations may include temporaland/or spatial noise reduction operations, which may be performed onvisible spectrum and/or infrared images, and which may include using aseries of images, for example, provided by one or both of imagingmodules 4002 a-b. FIG. 30 illustrates an infrared image 4600 comprisingthe infrared image 4500 of FIG. 29 after low pass filtering to reducenoise in accordance with an embodiment of the disclosure. In a furtherembodiment, a NUC process may be performed on the captured and storedimages to remove noise therein, for example, by using various NUCtechniques disclosed herein. In one embodiment, context data associatedwith infrared images may be analyzed to select blurred infrared images(e.g., motion-based and/or focus-based blurred thermal images) to beused by a NUC process described herein. In another embodiment, othercalibration processes for infrared images may be performed, such asprofiling, training, baseline parameter construction, and otherstatistical analysis on one or more images provided by one or both ofimaging modules 4002 a-b. Calibration parameters resulting from suchprocesses may be applied to images to correct, calibrate, or otherwiseadjust radiometric data in infrared images, for example, or to correctcolor or intensity data of one or more visible spectrum images.

In further embodiments, preprocessing operations may include operationsin which more general image characteristics may be normalized and/orcorrected. In one embodiment, an image may be analyzed to determine adistribution of intensities for one or more components of the image,such as a distribution of red intensities in an RGB color space image,or a distribution of luminance intensities in a YUV or YCbCr color spaceimage. An overall gain and/or offset may be determined for the imagebased on such a distribution, for example, and used to adjust thedistribution so that it matches an expected (e.g., corrected) or desired(e.g., targeted) distribution. In other embodiments, an overall gainand/or offset may be determined so that a particular interval of thedistribution utilizes more of the dynamic range of the particularcomponent or components of the image.

In some embodiments, a dynamic range of a first image (e.g., a dynamicrange of a radiometric component of an infrared image) may be normalizedto the dynamic range of a second image (e.g., a dynamic range of aluminance component of a visible spectrum image). In other embodiments,a dynamic range of a particular image may be adjusted according to ahistogram equalization method, a linear scaling method, or a combinationof the two, for example, to distribute the dynamic range according toinformation contained in a particular image or selection of images.

In further embodiments, adjustments and/or normalizations of dynamicranges or other aspects of images may be performed while retaining acalibration of a radiometric component of an infrared image. Forexample, a dynamic range of a non-radiometric component of an infraredimage may be adjusted without adjusting the dynamic range of theradiometric component of infrared image. In other embodiments, theradiometric component of an infrared image may be adjusted to emphasizea particular thermal interval, for example, and the adjustment may bestored with the infrared image so that accurate temperaturecorrespondence (e.g., a pseudo-color and/or intensity correspondence)may be presented to a user along with a user-viewable imagecorresponding to the thermal image and/or a combined image includinginfrared characteristics derived from the infrared image.

In other embodiments, preprocessing operations may include convertingvisible spectrum and/or infrared images to a different or common colorspace. For example, visible spectrum images and/or infrared images maybe converted from an RGB color space, for example, to a common YCbCrcolor space. In other embodiments, images in a raw or uncompressedformat may be converted to a common RGB or YCbCr color space. In someembodiments, a pseudo-color palette, such as a pseudo-color palettechosen by a user in block 4102, may be applied as part of thepreprocessing operations performed in block 4120. As with the dynamicrange adjustments, application of color palettes may be performed whileretaining a calibration of a radiometric component of an infrared image,for example, or a color space calibration of a visible spectrum image.

In another embodiment, preprocessing operations may include decomposingimages into various components. For example, an infrared image in acolor space/format including a raw or uncompressed radiometric componentmay be converted into an infrared image in a YCbCr color space. The rawradiometric component may be encoded into a luminance (e.g., Y)component of the converted infrared image, for example, or into achrominance (e.g., Cr and/or Cb) component of the converted infraredimage, or into the luminance and chrominance components of the convertedinfrared image. In some embodiments, unused components may be discarded,for example, or set to a known value (e.g., black, white, grey, or aparticular primary color). Visible spectrum images may also be convertedand decomposed into constituent components, for example, in a similarfashion. The decomposed images may be stored in place of the originalimages, for example, and may include context data indicating all colorspace conversions and decompositions so as to potentially retain aradiometric and/or color space calibration of the original images.

More generally, preprocessed images may be stored in place of originalimages, for example, and may include context data indicating all appliedpreprocessing operations so as to potentially retain a radiometricand/or color space calibration of the original images.

At block 4130, system 4000 may generate one or more combined images fromthe captured and/or preprocessed images. For example, one or more ofimaging modules 4002 a-b and/or processor 4010 may be configured togenerate combined images of scene 4030 from visible spectrum andinfrared images captured by imaging modules 4002 a-b. In one embodiment,the visible spectrum images may be captured prior to the infraredimages. In an alternative embodiment, the infrared images may becaptured prior to the visible spectrum images. Such combined images mayserve to provide enhanced imagery as compared to imagery provided by thevisible spectrum or infrared images alone.

In one embodiment, processor 4010 may be configured to generate combinedimages according to a true color mode, such as that described withrespect to blocks 4233, 4235, and 4238 of process 4200 illustrated bythe flowchart of FIG. 26. For example, a combined image may include aradiometric component of an infrared image of scene 4030 blended with acorresponding component of a visible spectrum image according to ablending parameter. In such embodiments, the remaining portions of thecombined image may be derived from corresponding portions of the visiblespectrum and/or infrared images of scene 4030.

In another embodiment, processor 4010 may be configured to generatecombined images according to a high contrast mode, such as thatdescribed with respect to blocks 4233, 4234, 4236, and 4238 of process4200 illustrated by the flowchart of FIG. 26. For example, a combinedimage may include a radiometric component of an infrared image and ablended component including infrared characteristics of scene 4030blended with high spatial frequency content, derived from visiblespectrum and/or infrared images, according to a blending parameter.

More generally, processor 4010 may be configured to generate combinedimages that increase or refine the information conveyed by either thevisible spectrum or infrared images viewed by themselves. Combinedimages may be stored in memory 4012, for example, for subsequentpost-processing and/or presentation to a user or a monitoring system,for instance, or may be used to generate control signals for one or moreother components 4018.

At block 4140, system 4000 may perform a variety of post-processingoperations on combined images. For example, one or more of imagingmodules 4002 a-b and/or processor 4010 may be configured to perform oneor more post-processing operations on combined images generated fromvisible spectrum and infrared characteristics of scene 4030, forexample, derived from images captured by imaging modules 4002 a-b.

Similar to the preprocessing operations described with respect to block4120, post-processing operations may include a variety of numerical,bit, and/or combinatorial operations performed on all or a portion of animage, such as on a component of an image, for example, or a selectionof pixels of an image, or on a selection or series of images. Forexample, any of the dynamic range adjustment operations described abovewith respect to preprocessing operations performed on captured imagesmay also be performed on one or more combined images. In one embodiment,a particular color-palette, such as a night or day-time palette, or apseudo-color palette, may be applied to a combined image. For example, aparticular color-palette may be designated by a user in block 4102, ormay be determined by context or other data, such as a current time ofday, a type of combined image, or a dynamic range of a combined image.

In other embodiments, post-processing operations may include adding highresolution noise to combined images in order to decrease an impressionof smudges or other artifacts potentially present in the combinedimages. In one embodiment, the added noise may include high resolutiontemporal noise (e.g., “white” signal noise). In further embodiments,post-processing operations may include one or more noise reductionoperations to reduce or eliminate noise or other non-physical artifactsintroduced into the combined images by image processing, for example,such as aliasing, banding, dynamic range excursion, and numericalcalculation-related bit-noise.

In some embodiments, post-processing operations may includecolor-weighted (e.g., chrominance-weighted) adjustments to luminancevalues of an image in order to ensure that areas with extensive colordata are emphasized over areas without extensive color data. Forexample, where a radiometric component of an infrared image is encodedinto a chrominance component of a combined image, in block 4130, forexample, a luminance component of the combined image may be adjusted toincrease the luminance of areas of the combined image with a high levelof radiometric data. A high level of radiometric data may correspond toa high temperature or temperature gradient, for example, or an area ofan image with a broad distribution of different intensity infraredemissions (e.g., as opposed to an area with a narrow or unitarydistribution of intensity infrared emissions). Other normalizedweighting schemes may be used to shift a luminance component of acombined image for pixels with significant color content. In alternativeembodiments, luminance-weighted adjustments to chrominance values of animage may be made in a similar manner.

More generally, post-processing operations may include using one or morecomponents of a combined image to adjust other components of a combinedimage in order to provide automated image enhancement. In someembodiments, post-processing operations may include adjusting a dynamicrange, a resolution, a color space/format, or another aspect of combinedimages to match or approximate a corresponding aspect of a display, forexample, or a corresponding aspect expected by a monitoring system orselected by a user.

Post-processed combined images may be stored in place of originalcombined images, for example, and may include context data indicatingall applied post-processing operations so as to potentially retain aradiometric and/or color space calibration of the original combinedimages.

At block 4150, system 4000 may generate control signals related to thecombined images. For example, processor 4010 may be configured togenerate control signals adapted to energize and/or operate any of analarm, a siren, a messaging system, a security light, or one or more ofother components 4018, according to conditions detected from theenhanced imagery provided by the combined images. Such control signalsmay be generated when a combined image contains a detected object orcondition, such as one or more of pedestrians 4050 and/or vehicle 4040entering or idling in scene 4030, for example. In other embodiments,processor 4010 may be configured to generate control signals notifying amonitoring system of detected objects or conditions in scene 4030.

At block 4152, system 4000 may display images to a user. For example,processor 4010 may be configured to convert visible spectrum, infrared,and/or combined images (e.g., from block 4130 and/or 4140) intouser-viewable combined images and present the user-viewable combinedimages to a user utilizing display 4016. In other embodiments, processor4010 may also be configured to transmit combined images, includinguser-viewable combined images, to a monitoring system (e.g., usingcommunication module 4014) for further processing, notification, controlsignal generation, and/or display to remote users. As noted above,embodiments of process 4100 may include additional embodiments of block4152, for example. In some embodiments, one or more embodiments of block4152 may be implemented as part of one or more feedback loops, forexample, which may include embodiments of blocks 4102 and/or 4104.

At block 4154, system 4000 may store images and other associated data.For example, processor 4010 may be configured to store one or more ofthe visible spectrum, infrared, or combined images, including associatedcontext data and other data indicating pre-and-post-processingoperations, to memory 4012, for example, or to an external or portablememory device.

Turning now to FIG. 26, FIG. 26 illustrates a flowchart of a process4200 to enhance infrared imaging of a scene in accordance with anembodiment of the disclosure. For example, one or more portions ofprocess 4200 may be performed by processor 4010 and/or each of imagingmodules 4002 a-b of system 4000 and utilizing any of memory 4012,communication module 4014, display 4016, or other components 4018. Insome embodiments, process 4200 may be implemented as an embodiment ofblock 4130 in process 4100 of FIG. 25, for example, to generate combinedimages from captured infrared and/or visible spectrum images received inblocks 4112 and/or 4118 in process 4100.

It should be appreciated that system 4000 and scene 4030 are identifiedonly for purposes of giving examples and that any other suitable systemincluding images of any other type of scene may perform all or part ofprocess 4200. It should also be appreciated that any step, sub-step,sub-process, or block of process 4200 may be performed in an orderdifferent from the embodiment illustrated by FIG. 26, and, furthermore,may be performed before, after, or within one or more blocks in process4100 of FIG. 25, including blocks other than block 4130. For example,although process 4200 describes distinct blending and high-contrastmodes, in other embodiments, captured images may be combined using anyportion, order, or combination of the blending and/or high-contrast modeprocessing operations.

At block 4230, system 4000 may receive preprocessed visible spectrum andinfrared images. For example, processor 4010 of system 4000 may beconfigured to receive one or more visible spectrum images from visiblespectrum imaging module 4002 a and one or more infrared images frominfrared imaging module 4002 b. In one embodiment, visible spectrumand/or infrared images may be preprocessed according to block 4120 ofFIG. 25. Once the visible spectrum and infrared images are received byprocessor 4010, processor 4010 may be configured to determine a mode forgenerating combined images. Such mode may be selected by a user in block4102 of FIG. 25, for example, or may be determined according to contextdata or an alternating mode, for instance, where the mode of operationalternates between configured modes upon a selected schedule or aparticular monitoring system expectation.

In the embodiment illustrated by FIG. 26, processor 4010 may determine atrue color mode, including one or more of blocks 4233 and 4235, or ahigh contrast mode, including one or more of blocks 4232, 4234, and4236. In other embodiments, process 4200 may include other selectablemodes including processes different from those depicted in FIG. 26, forexample, or may include only a single mode, such as a mode including oneor more adjustable blending parameters. In embodiments with multiplepossible modes, once a mode is determined, process 4200 may proceed withthe selected mode.

At block 4233, system 4000 may perform various pre-combining operationson one or more of the visible spectrum and infrared images. For example,if a true color mode is determined in block 4230, processor 4010 may beconfigured to perform pre-combining operations on one or more visiblespectrum and/or infrared images received in block 4230. In oneembodiment, pre-combining operations may include any of thepre-processing operations described with respect to block 4120 of FIG.25. For example, the color spaces of the received images may beconverted and/or decomposed into common constituent components.

In other embodiments, pre-combining operations may include applying ahigh pass filter, applying a low pass filter, a non-linear low passfiler (e.g., a median filter), adjusting dynamic range (e.g., through acombination of histogram equalization and/or linear scaling), scalingdynamic range (e.g., by applying a gain and/or an offset), and addingimage data derived from these operations to each other to form processedimages. For example, a pre-combining operation may include extractingdetails and background portions from a radiometric component of aninfrared image using a high pass spatial filter, performing histogramequalization and scaling on the dynamic range of the background portion,scaling the dynamic range of the details portion, adding the adjustedbackground and details portions to form a processed infrared image, andthen linearly mapping the dynamic range of the processed infrared imageto the dynamic range of display 4016. In one embodiment, the radiometriccomponent of the infrared image may be a luminance component of theinfrared image. In other embodiments, such pre-combining operations maybe performed on one or more components of visible spectrum images.

As with other image processing operations, pre-combining operations maybe applied in a manner so as to retain a radiometric and/or color spacecalibration of the original received images. Resulting processed imagesmay be stored temporarily (e.g., in memory 4012) and/or may be furtherprocessed according to block 4235.

At block 4235, system 4000 may blend one or more visible spectrum imageswith one or more infrared images. For example, processor 4010 may beconfigured to blend one or more visible spectrum images of scene 4030with one or more infrared images of scene 4030, where the one or morevisible spectrum and/or infrared images may be processed versions (e.g.,according to block 4233) of images originally received in block 4230.

In one embodiment, blending may include adding a radiometric componentof an infrared image to a corresponding component of a visible spectrumimage, according to a blending parameter. For example, a radiometriccomponent of an infrared image may be a luminance component (e.g., Y) ofthe infrared image. In such an embodiment, blending the infrared imagewith a visible spectrum image may include proportionally adding theluminance components of the images according to a blending parameter andthe following first blending equation:Y _(CI) =ζ*Y _(VSI)+(1−ζ)*Y _(IRI)where Y_(CI) is the luminance component of the combined image, Y_(VSI)is the luminance of the visible spectrum image, Y_(IRI) is the luminancecomponent of the infrared image, and ζ varies from 0 to 1. In thisembodiment, the resulting luminance component of the combined image isthe blended image data.

In other embodiments, where a radiometric component of an infrared imagemay not be a luminance component of the infrared image, blending aninfrared image with a visible spectrum image may include addingchrominance components of the images according to the first blendingequation (e.g., by replacing the luminance components with correspondingchrominance components of the images), and the resulting chrominancecomponent of the combined image is blended image data. More generally,blending may include adding (e.g., proportionally) a component of aninfrared image, which may be a radiometric component of the infraredimage, to a corresponding component of a visible spectrum image. Onceblended image data is derived from the components of the visiblespectrum and infrared images, the blended image data may be encoded intoa corresponding component of the combined image, as further describedwith respect to block 4238. In some embodiments, encoding blended imagedata into a component of a combined image may include additional imageprocessing steps, for example, such as dynamic range adjustment,normalization, gain and offset operations, and color space conversions,for instance.

In embodiments where radiometric data is encoded into more than onecolor space/format component of an infrared image, the individual colorspace/format components of the infrared and visible spectrum images maybe added individually, for example, or the individual color spacecomponents may be arithmetically combined prior to adding the combinedcolor space/format components.

In further embodiments, different arithmetic combinations may be used toblend visible spectrum and infrared images. For example, blending aninfrared image with a visible spectrum image may include adding theluminance components of the images according to a blending parameter andthe following second blending equation:Y _(CI) =ζ*Y _(VSI) +Y _(IRI)where Y_(CI), Y_(VSI), and Y_(IRI) are defined as above with respect tothe first blending equation, and ζ varies from 0 to values greater thana dynamic range of an associated image component (e.g., luminance,chrominance, radiometric, or other image component). As with the firstblending equation, the second blending equation may be used to blendother components of an infrared image with corresponding components of avisible spectrum image. In other embodiments, the first and secondblending equations may be rewritten to include per-pixel color-weightingor luminance-weighting adjustments of the blending parameter, forexample, similar to the component-weighted adjustments described withrespect to block 4140 of FIG. 25, in order to emphasize an area with ahigh level of radiometric data.

In some embodiments, image components other than those corresponding toa radiometric component of an infrared image may be truncated, set to aknown value, or discarded. In other embodiments, the combined imagecomponents other than those encoded with blended image data may beencoded with corresponding components of either the visible spectrum orthe infrared images. For example, in one embodiment, a combined imagemay include a chrominance component of a visible spectrum image encodedinto a chrominance component of the combined image and blended imagedata encoded into a luminance component of the combined image, where theblended image data comprises a radiometric component of an infraredimage blended with a luminance component of the visible spectrum image.In alternative embodiments, a combined image may include a chrominancecomponent of the infrared image encoded into a chrominance component ofthe combined image.

A blending parameter value may be selected by a user (e.g., in block4102 of FIG. 25), or may be determined by processer 4010 according tocontext or other data, for example, or according to an image enhancementlevel expected by a coupled monitoring system. In some embodiments, theblending parameter may be adjusted or refined using a knob, joystick, orkeyboard coupled to processor 4010, for example, while a combined imageis being displayed by display 4016. From the first and second blendingequations, in some embodiments, a blending parameter may be selectedsuch that blended image data includes only infrared characteristics, or,alternatively, only visible spectrum characteristics.

In addition to or as an alternative to the processing described above,processing according to a true color mode may include one or moreprocessing steps, ordering of processing steps, arithmetic combinations,and/or adjustments to blending parameters as disclosed in U.S. patentapplication Ser. No. 12/477,828. For example, blending parameter ζ maybe adapted to affect the proportions of two luminance components of aninfrared image and a visible spectrum image. In one aspect, ζ may benormalized with a value in the range of 0 (zero) to 1, wherein a valueof 1 produces a blended image (e.g., blended image data, and/or acombined image) that is similar to the visible spectrum image. On theother hand, if ζ is set to 0, the blended image may have a luminancesimilar to the luminance of the infrared image. However, in the latterinstance, the chrominance (Cr and Cb) from the visible image may beretained. Each other value of ζ may be adapted to produce a blendedimage where the luminance part (Y) includes information from both thevisible spectrum and infrared images. For example, ζ may be multipliedto the luminance part (Y) of the visible spectrum image and added to thevalue obtained by multiplying the value of 1−ζ to the luminance part (Y)of the infrared image. This added value for the blended luminance parts(Y) may be used to provide the blended image (e.g., the blended imagedata, and/or the combined image).

In one embodiment, a blending algorithm may be referred to as true colorinfrared imagery. For example, in daytime imaging, a blended image maycomprise a visible spectrum color image, which includes a luminanceelement and a chrominance element, with its luminance value replaced bythe luminance value from an infrared image. The use of the luminancedata from the infrared image causes the intensity of the true visiblespectrum color image to brighten or dim based on the temperature of theobject. As such, the blending algorithm provides IR imaging for daytimeor visible light images.

After one or more visible spectrum images are blended with one or moreinfrared images, processing may proceed to block 4238, where blendeddata may be encoded into components of the combined images in order toform the combined images.

At block 4232, system 4000 may derive high spatial frequency contentfrom one or more of the visible spectrum and infrared images. Forexample, if a high contrast mode is determined in block 4230, processor4010 may be configured to derive high spatial frequency content from oneor more of the visible spectrum and infrared images received in block4230.

In one embodiment, high spatial frequency content may be derived from animage by performing a high pass filter (e.g., a spatial filter)operation on the image, where the result of the high pass filteroperation is the high spatial frequency content. FIG. 31 illustrates animage 4700 comprising high spatial frequency content derived from avisible spectrum image using high pass filtering in accordance with anembodiment of the disclosure. In an alternative embodiment, high spatialfrequency content may be derived from an image by performing a low passfilter operation on the image, and then subtracting the result from theoriginal image to get the remaining content, which is the high spatialfrequency content. In another embodiment, high spatial frequency contentmay be derived from a selection of images through difference imaging,for example, where one image is subtracted from a second image that isperturbed from the first image in some fashion, and the result of thesubtraction is the high spatial frequency content. For example, one orboth of optical elements 4004 a-b may be configured to introducevibration, focus/de-focus, and/or movement artifacts into a series ofimages captured by one or both of imaging modules 4002 a-b. High spatialfrequency content may be derived from subtractions of adjacent orsemi-adjacent images in the series.

In some embodiments, high spatial frequency content may be derived fromonly the visible spectrum images or the infrared images. In otherembodiments, high spatial frequency content may be derived from only asingle visible spectrum or infrared image. In further embodiments, highspatial frequency content may be derived from one or more components ofvisible spectrum and/or infrared images, such as a luminance componentof a visible spectrum image, for example, or a radiometric component ofan infrared image. Resulting high spatial frequency content may bestored temporarily (e.g., in memory 4012) and/or may be furtherprocessed according to block 4236.

At block 4234, system 4000 may de-noise one or more infrared images. Forexample, processor 4010 may be configured to de-noise, smooth, or blurone or more infrared images of scene 4030 using a variety of imageprocessing operations. In one embodiment, removing high spatialfrequency noise from infrared images allows processed infrared images tobe combined with high spatial frequency content derived according toblock 4232 with significantly less risk of introducing double edges(e.g., edge noise) to objects depicted in combined images of scene 4030.

In one embodiment, removing noise from infrared images may includeperforming a low pass filter (e.g., a spatial and/or temporal filter)operation on the image, where the result of the low pass filteroperation is a de-noised or processed infrared image. FIG. 30illustrates processed image 4600 resulting from low pass filteringinfrared image 4500 of FIG. 29 to reduce noise in accordance with anembodiment of the disclosure. In a further embodiment, removing noisefrom one or more infrared images may include down-sampling the infraredimages and then up-sampling the images back to the original resolution.

In another embodiment, processed infrared images may be derived byactively blurring infrared images of scene 4030. For example, opticalelements 4004 b may be configured to slightly de-focus one or moreinfrared images captured by infrared imaging module 4002 b. Theresulting intentionally blurred infrared images may be sufficientlyde-noised or blurred so as to reduce or eliminate a risk of introducingdouble edges into combined images of scene 4030, as further describedbelow. In other embodiments, blurring or smoothing image processingoperations may be performed by processor 4010 on infrared imagesreceived at block 4230 as an alternative or supplement to using opticalelements 4004 b to actively blur infrared images of scene 4030.Resulting processed infrared images may be stored temporarily (e.g., inmemory 4012) and/or may be further processed according to block 4236.

At block 4236, system 4000 may blend high spatial frequency content withone or more infrared images. For example, processor 4010 may beconfigured to blend high spatial frequency content derived in block 4232with one or more infrared images of scene 4030, such as the processedinfrared images provided in block 4234.

In one embodiment, high spatial frequency content may be blended withinfrared images by superimposing the high spatial frequency content ontothe infrared images, where the high spatial frequency content replacesor overwrites those portions of the infrared images corresponding towhere the high spatial frequency content exists. For example, the highspatial frequency content may include edges of objects depicted inimages of scene 4030, but may not exist within the interior of suchobjects. In such embodiments, blended image data may simply include thehigh spatial frequency content, which may subsequently be encoded intoone or more components of combined images, as described in block 4238.FIG. 32 illustrates a combined image 4800 comprising a combination ofthe low pass filtered infrared image 4600 of FIG. 30 with the high passfiltered visible spectrum image 4700 of FIG. 31 generated in accordancewith an embodiment of the disclosure.

For example, a radiometric component of an infrared image may be achrominance component of the infrared image, and the high spatialfrequency content may be derived from the luminance and/or chrominancecomponents of a visible spectrum image. In this embodiment, a combinedimage may include the radiometric component (e.g., the chrominancecomponent of the infrared image) encoded into a chrominance component ofthe combined image and the high spatial frequency content directlyencoded (e.g., as blended image data but with no infrared imagecontribution) into a luminance component of the combined image. By doingso, a radiometric calibration of the radiometric component of theinfrared image may be retained. In similar embodiments, blended imagedata may include the high spatial frequency content added to a luminancecomponent of the infrared images, and the resulting blended data encodedinto a luminance component of resulting combined images.

In other embodiments, high spatial frequency content may be derived fromone or more particular components of one or a series of visible spectrumand/or infrared images, and the high spatial frequency content may beencoded into corresponding one or more components of combined images.For example, the high spatial frequency content may be derived from aluminance component of a visible spectrum image, and the high spatialfrequency content, which in this embodiment is all luminance image data,may be encoded into a luminance component of a combined image.

In another embodiment, high spatial frequency content may be blendedwith infrared images using a blending parameter and an arithmeticequation, such as the first and second blending equations, above. Forexample, in one embodiment, the high spatial frequency content may bederived from a luminance component of a visible spectrum image. In suchan embodiment, the high spatial frequency content may be blended with acorresponding luminance component of an infrared image according to ablending parameter and the second blending equation to produce blendedimage data. The blended image data may be encoded into a luminancecomponent of a combined image, for example, and the chrominancecomponent of the infrared image may be encoded into the chrominancecomponent of the combined image. In embodiments where the radiometriccomponent of the infrared image is its chrominance component, thecombined image may retain a radiometric calibration of the infraredimage. In other embodiments, portions of the radiometric component maybe blended with the high spatial frequency content and then encoded intoa combined image.

More generally, the high spatial frequency content may be derived fromone or more components of a visible spectrum image and/or an infraredimage. In such an embodiment, the high spatial frequency content may beblended with one or more components of the infrared image to produceblended image data (e.g., using a blending parameter and a blendingequation), and a resulting combined image may include the blended imagedata encoded into corresponding one or more components of the combinedimage. In some embodiments, the one or more components of the blendeddata do not have to correspond to the eventual one or more components ofthe combined image (e.g., a color space/format conversion may beperformed as part of an encoding process).

A blending parameter value may be selected by a user (e.g., in block4102 of FIG. 25), or may be automatically determined by processor 4010according to context or other data, for example, or according to animage enhancement level expected by a coupled monitoring system. In someembodiments, the blending parameter may be adjusted or refined using aknob coupled to processor 4010, for example, while a combined image isbeing displayed by display 4016. In some embodiments, a blendingparameter may be selected such that blended image data includes onlyinfrared characteristics, or, alternatively, only visible spectrumcharacteristics. A blending parameter may also be limited in range, forexample, so as not to produce blended data that is out-of-bounds withrespect to a dynamic range of a particular color space/format or adisplay.

In addition to or as an alternative to the processing described above,processing according to a high contrast mode may include one or moreprocessing steps, ordering of processing steps, arithmetic combinations,and/or adjustments to blending parameters as disclosed in U.S. patentapplication Ser. No. 13/437,645. For example, the following equationsmay be used to determine the components Y, Cr and Cb for the combinedimage with the Y component from the high pass filtered visible spectrumimage and the Cr and Cb components from the infrared image.

hp_y_vis=highpass(y_vis)

(y_ir, cr_ir, cb_ir)=colored(lowpass(ir_signal_linear))

which in another notation could be written as:

hp_(y) _(vis) =highpass(y_(vis))

(y_(ir), cr_(ir), cb_(ir))=colored(lowpass(ir_(signal linear)))

In the above equations, highpass(y_vis) may be high spatial frequencycontent derived from high pass filtering a luminance component of avisible spectrum image. Colored(lowpass(ir_signal_linear)) may be theresulting luminance and chrominance components of the infrared imageafter the infrared image is low pass filtered. In some embodiments, theinfrared image may include a luminance component that is selected to be0.5 times a maximum luminance (e.g., of a display and/or a processingstep). In related embodiments, the radiometric component of the infraredimage may be the chrominance component of the infrared image. In someembodiments, the y_ir component of the infrared image may be dropped andthe components of the combined image may be (hp_y_vis, cr_ir, cb_ir),using the notation above.

In another embodiment, the following equations may be used to determinethe components Y, Cr and Cb for a combined image with the Y componentfrom the high pass filtered visible spectrum image and the Cr and Cbcomponents from the infrared image.

comb_y=y_ir+alpha×hp_y_vis

comb_cr=cr_ir

comb_cb=cb_ir

which in another notation could be written as:

comb_(y)=y_(ir)+alpha*hp_(y) _(vis)

comb_(cr)=cr_(ir)

comb_(cb)=cb_(ir)

The variation of alpha thus gives the user an opportunity to decide howmuch contrast is needed in the combined image. With an alpha of close tozero, the IR image alone will be shown, but with a very high alpha, verysharp contours can be seen in the combined image. Theoretically, alphacan be an infinitely large number, but in practice a limitation willprobably be necessary, to limit the size of alpha that can be chosen towhat will be convenient in the current application. In the aboveequations, alpha may correspond to a blending parameter

Once the high spatial frequency content is blended with one or moreinfrared images, processing may proceed to block 4238, where blendeddata may be encoded into components of the combined images in order toform the combined images.

At block 4238, system 4000 may encode the blended data into one or morecomponents of the combined images. For example, processor 4010 may beconfigured to encode blended data derived or produced in accordance withblocks 4235 and/or 4236 into a combined image that increases, refines,or otherwise enhances the information conveyed by either the visiblespectrum or infrared images viewed by themselves.

In some embodiments, encoding blended image data into a component of acombined image may include additional image processing steps, forexample, such as dynamic range adjustment, normalization, gain andoffset operations, noise reduction, and color space conversions, forinstance. FIG. 34 illustrates a combined image 5000 comprising the lowresolution infrared image 4900 of FIG. 33 after being resampled,processed, and combined with high spatial frequency content derived froma visible spectrum image of the scene in accordance with an embodimentof the disclosure. In addition, processor 4010 may be configured toencode other image data into combined images.

For example, if blended image data is encoded into a luminance componentof a combined image, a chrominance component of either a visiblespectrum image or an infrared image may be encoded into a chrominancecomponent of a combined image. Selection of a source image may be madethrough user input, for example, or may be determined automaticallybased on context or other data. More generally, in some embodiments, acomponent of a combined image that is not encoded with blended data maybe encoded with a corresponding component of a visible spectrum image oran infrared image. By doing so, a radiometric calibration of an infraredimage and/or a color space calibration of a visible spectrum image maybe retained in the resulting combined image. Such calibrated combinedimages may be used for enhanced infrared imaging applications,particularly where constituent visible spectrum images and infraredimages of a scene are captured at different times and/or disparateambient lighting levels.

FIG. 35 illustrates a combined image 5100 generated in accordance withan embodiment of the disclosure. The combined image of FIG. 35 is in theform of a picture-in-picture combined image including a relatively lowresolution infrared image 5102 captured at a first time enhanced withhigh spatial frequency content 5106 of a portion 5104 of a visiblespectrum image captured at a second time. FIG. 36 illustrates a combinedimage 5204 generated in accordance with another embodiment of thedisclosure. Combined image 5204 is in the form of a scaled portion 5202of infrared image 5200 captured at a first time combined with highspatial frequency content of a visible spectrum image captured at asecond time.

Turning now to FIG. 27, FIG. 27 illustrates a flowchart of a process4300 to enhance infrared imaging of a scene in accordance with anembodiment of the disclosure. For example, one or more portions ofprocess 4300 may be performed by processor 4010 and/or each of imagingmodules 4002 a-b of system 4000 and utilizing any of memory 4012,communication module 4014, display 4016, or other components 4018.

It should be appreciated that system 4000 and scene 4030 are identifiedonly for purposes of giving examples and that any other suitable systemincluding images of any other type of scene may perform all or part ofprocess 4300. It should also be appreciated that any step, sub-step,sub-process, or block of process 4300 may be performed in an orderdifferent from the embodiment illustrated by FIG. 27, and, furthermore,may be performed before, after, or in parallel with one or more blocksin process 4100 of FIG. 25 and/or process 4200 of FIG. 26. For example,although process 4300 describes receiving user input after capturingimages, performing triple fusion processing operations, and/ordisplaying images to a user, in other embodiments, user input may bereceived at any point or points within process 4300.

At block 4310, system 4000 may capture visible spectrum and infraredimages. For example, processor 4010 and/or imaging modules 4002 a-b ofsystem 4000 may be configured to capture one or more visible spectrumimages and infrared images of scene 4030. In some embodiments, block4310 may correspond to one or more of blocks 4110-4118 of process 4100in FIG. 25. In other embodiments, visible spectrum images may becaptured substantially simultaneously with infrared images. Once atleast one visible spectrum image and/or infrared image is captured,process 4300 may continue to block 4320.

At block 4320, system 4000 may perform triple fusion processingoperations on one or more captured images of scene 4030 to generatecombined images (e.g., at least three processes performed on such imagesin some embodiments). For example, processor 4010 of system 4000 may beconfigured to perform adjustable scene-based NUC processing (e.g., block4322), true color processing (e.g., block 4324), and high contrastprocessing (e.g., block 4326) on one or more images captured in block4310, and then generate corresponding combined images of scene 4030including relative contributions of the various processing operations.In some embodiments, the relative contributions to the combined imagesmay be determined by one or more control parameters. In suchembodiments, control parameters may be determined from one or more userinputs, threshold values, and/or from other operating parameters ofsystem 4000. In some embodiments, greater or fewer than three processingoperations may be performed in block 4320. In some embodiments, otherprocessing operations may be performed in block 4320 instead of and/orin addition to the illustrated operations.

For example, a relative contribution of a particular processingoperation may include one or more components of an intermediary image,such as a result of a true color processing operation (e.g., processingaccording to a true color mode in FIG. 26), for example, multiplied by acorresponding control parameter. The one or more control parameters usedto determine the relative contributions may be interdependent, forexample, so that a resulting combined image from block 4320 includesimage components (e.g., luminance, chrominance, radiometric, or othercomponents) within a dynamic range of a particular display or a dynamicrange expected by a communicatively coupled monitoring system, forinstance. In other embodiments, one control parameter may be used toarithmetically determine all relative contributions, similar to variousblending processing operations described herein. In further embodiments,multiple control parameters may be used to determine one or morerelative contributions, and post-processing operations (e.g., similar toembodiments of block 4140 of FIG. 25) may be used to adjust a dynamicrange of a resulting combined image appropriately.

In still further embodiments, control parameters may be determined on apixel-by-pixel basis, using threshold values, for example. Suchthreshold values may be used to compensate for too low and/or too highluminosity and/or chrominance values of intermediary and/or combinedimages, for example, or to select portions of intermediary imagesassociated with particular control parameters and/or relativecontributions. For example, in one embodiment, one or more thresholdvalues may be used to apply a control parameter of 0.75 (e.g., 75% ofthe total contribution to a combined image) to portions of a true colorprocessed intermediary image with luminance values above a median value(e.g., the threshold value in this embodiment), and apply a controlparameter of 0.25 (e.g., 25% of the total contribution to the combinedimage) to portions of the true color processed intermediary image withluminance values below the median value.

In one embodiment, processor 4010 of system 4000 may be configured toreceive control parameters for processing captured images from memory4012, derive color characteristics of scene 4030 from at least onecaptured visible spectrum image and/or infrared image, derive highspatial frequency content from at least one captured visible spectrumimage and/or infrared image (e.g., which may be the same or differentfrom the image(s) used to derive the color characteristics of thescene), and generate a combined image including relative contributionsof the color characteristics and the high spatial frequency content thatare determined by one or more of the control parameters.

In some embodiments, deriving color characteristics of scene 1430 (e.g.,true color processing 4324) may involve one or more processing stepssimilar to those discussed in reference to blocks 4233, 4235, and 4238(e.g., a true color mode) of FIG. 26. In other embodiments, derivinghigh spatial frequency content (e.g., high contrast processing 4326) mayinvolve one or more processing steps similar to those discussed inreference to blocks 4232, 4234, 4236, and 4238 (e.g., a high contrastmode) of FIG. 26. In further embodiments, any one or combination ofscene-based NUC processing 4322, true color processing 4324, and highcontrast processing 4326 may include one or more processing steps thesame and/or similar to those discussed in reference to blocks 4110-4140of FIG. 25. In addition, other image analytics and processing may beperformed in place of or in addition to blocks 4322, 4324, and/or 4326,according to methodologies provided in U.S. patent application Ser. No.12/477,828 and/or U.S. patent application Ser. No. 13/437,645.

In one embodiment, processor 4010 may be configured to selectively applyscene-based NUC processing to one or more infrared images by enabling ordisabling the correction, for example, or by implementing correctionsaccording to an overall gain applied to all or at least a portion of thecorrection terms. In some embodiments, the selectivity may be related to(e.g., proportional to) a control parameter, for example. In anotherembodiment, infrared imaging module 4002 b may be configured toselectively apply scene-based NUC processing to one or more infraredimages.

In some embodiments, true color processing 4324 may include derivingcolor characteristics of scene 4030 from a chrominance component of avisible spectrum image of scene 4030 captured by visible spectrumimaging module 4002 a. In such embodiments, a combined image may includea relative contribution from a luminance component of an infrared imageof scene 4030 captured by infrared imaging module 4002 b, where therelative contribution (e.g., its multiplicative factor, gain, orstrength) of the luminance component of the infrared image substantiallymatches the relative contribution of the color characteristics. In someembodiments, the control parameter determining the relative contributionof the color characteristics may be used to determine the relativecontribution of the luminance component of the infrared image. Infurther embodiments, the luminance component of the infrared image maybe blended with a luminance component of the visible spectrum imagebefore the resulting blended image data is contributed to the combinedimage in place of the luminance component of the infrared image. Invarious embodiments, a luminance component of the infrared image maycorrespond to a radiometric component of the infrared image.

In some embodiments, high contrast processing 4326 may include derivinghigh spatial frequency content from a luminance component of a visiblespectrum image of scene 4030 captured by visible spectrum imaging module4002 a. In such embodiments, a combined image may include a relativecontribution from a chrominance component of an infrared image of scene4030 captured by infrared imaging module 4002 b, where the relativecontribution of the chrominance component of the infrared imagesubstantially matches the relative contribution of the high spatialfrequency content. In some embodiments, the control parameterdetermining the relative contribution of the high spatial frequencycontent may be used to determine the relative contribution of thechrominance component of the infrared image. In further embodiments,high spatial frequency content may be blended with a luminance componentof the infrared image before the resulting blended image data iscontributed to the combined image in place of the high spatial frequencycontent. In various embodiments, a chrominance component of the infraredimage may correspond to a radiometric component of the infrared image.

In various embodiments, the processing of blocks 4322, 4324, and 4326may be performed in any desired order (e.g., serial, parallel, orcombinations thereof).

Once one or more combined images are generated by processing in block4320, process 4300 may move to block 4330 to display the combined imagesto a user.

At block 4330, system 4000 may display images to a user. For example,processor 4010 of system 4000 may be configured to use display 4016 todisplay combined images generated from block 4320, captured and/orprocessed images from block 4310, or other images, for example, such asimages stored in memory 4012. In some embodiments, processor 4010 may beconfigured to display information, such as text indicating a relativecontribution and/or a blending parameter value, for example, in additionto one or more images. In further embodiments, where display 4016 isimplemented as a touchscreen display, for example, processor 4010 may beconfigured to display images representing user input devices that acceptuser input, such as slider controls, selectable buttons or regions,rotatable knobs, and other user input devices. In some embodiments,block 4330 may correspond to block 4152 of process 4100 in FIG. 25. Invarious embodiments, one or more embodiments of block 4330 may beimplemented as part of one or more feedback loops, for example, whichmay include embodiments of blocks 4102 and/or 4104 of process 4100 inFIG. 25.

At block 4340, system 4000 may receive user input and/or machine-basedinput. For example, processor 4010 of system 4000 may be configured toreceive user input from a user interface that selects, adjusts, refines,or otherwise determines one or more control parameters used to generatecombined images according to block 4320. In some embodiments, such userinput may select, adjust, refine, or otherwise determine one or moreblending parameters, for example, such as a blending parameter for truecolor processing in block 4324 or a blending parameter for high contrastprocessing in block 4326. In other embodiments, such user input mayselect, adjust, refine, or otherwise determine a control parameter forselectively applying scene-based NUC processing in block 4322, asdescribed herein. User input may be received from one or more user inputdevices of a user interface, such as a slider control, a knob orjoystick, a button, or images of such controls displayed by atouchscreen display. Other types of user input devices are alsocontemplated. In some embodiments, user input may relate to a selectedexit point of process 4300, for example, or an entry or re-entry pointfor one or more of processes 4100, 4200, and/or 4300. In one embodiment,once system 4000 receives user input, process 4300 may continue to block4310, where already-captured images may be re-processed and/orre-displayed according to updated control, blending, or other operatingparameters, for example, or where newly-captured images may be processedand displayed, as described herein.

In further embodiments, in addition to, or alternatively, processor 4010of system 4000 may be configured to receive machine-based input (e.g.,information provided from one or more other components 4018 and/or anycomponents described herein), and be adapted to select, adjust, refine,or otherwise determine one or more control parameters (e.g., blendingparameters, control parameters for NUC processing, or other operationalparameters, for example) used to generate combined images according toblock 4320. In some embodiments, such parameters may be used to select aparticular processing methodology, or morph from one selection ofprocessing methodology to another, as described herein. In variousembodiments, the machine-based input may relate to a particular time ofday, a level of ambient light, an environmental temperature, otherenvironmental conditions, and/or other data provided by various types ofsensors, for example. In other embodiments, the machine-based input maybe provided as a result of processing of one or more images, forexample, in accordance with various techniques described herein.

In one embodiment, process 4300 may be adapted to capture visiblespectrum and/or infrared images (e.g., block 4310), derive high spatialfrequency content and/or visible spectrum color data (e.g., achrominance component) from the visible spectrum images (e.g., portionsof blocks 4324 and 4326), and blend the high spatial frequency contentand visible spectrum color with corresponding infrared images (e.g.,portions of blocks 4320-4326, and where the infrared images may beprocessed according to various NUC processing techniques, as in block4322), to form combined images including various aspects of enhancedinfrared imagery.

Referring now to FIG. 28, FIG. 28 shows a user interface 4400 forimaging system 4000 in accordance with an embodiment of the disclosure.User interface 4400 may include interface housing 4410, display 4416,one or more slider controls 4418-4419, and/or knob/joystick 4424. Insome embodiments, user interface 4400 may correspond to one or more ofdisplay 4016 and other components 4018 of system 4000 in FIG. 24. Forexample, display 4416 may be implemented as a touchscreen display anduser interface 4400 may include user-selectable images of user inputdevices, such as slider control 4420 and/or selectable text box 4422.Each user input device may accept user input to determine a method ofgenerating combined images, to select a radiometric interval, to inputcontext and/or sensor data, to select a color or pseudo-color palettefor one or more image types, to select or refine a blending parameter,to select or refine a control parameter, to select or refine thresholdvalues, and/or to determine other operating parameters of system 4000,as described herein.

As shown in FIG. 28, display 4416 may be configured to display one ormore combined images 4430, or a series of combined images (e.g., video)processed according to image analytics and other processing operationsdescribed herein. In some embodiments, display 4416 may be configured todisplay triple fusion processed combined images, true color processedcombined or intermediary images, high contrast processed combined orintermediary images, NUC processed images, unprocessed captured images,and/or captured images processed according to any of the processingoperations described herein.

In one embodiment, a user may adjust a user input device (e.g., bymoving a slider up, down, left, or right, by rotating a knob, bymanipulating a joystick, by selecting an image or region displayed by atouchscreen) and user interface 4400 and/or display 4014 may providevisible feedback related to the user input by displaying text inselectable text box 4422, for example, or by updating (e.g., using othercomponents of system 4000) imagery displayed by display 4416. In oneembodiment, a user may select a relative contribution (e.g., of aparticular image processing technique, such as NUC processing, highcontrast processing, true color processing, various pre-processing,various post-processing, and/or any other processing described herein)to adjust by selecting an image or region (e.g., a rectangular regionwithin selectable text box 4422, shown as solid when selected, forexample) in selectable text box 4422, for example, and then adjust acontrol parameter of that relative contribution by manipulating one ormore of slider controls 4418-4420 and/or knob/joystick 4424.

For example, in some embodiments, one of slider controls 4418-4420 maybe assigned to and vary a control parameter that is used to set therelative gain (e.g., a relative contribution) applied to one or morecomponents of an intermediary image resulting from high contrastprocessing (e.g., block 4326), for instance, and thereby vary acontribution of the high contrast processing in the user-displayedimage. In other embodiments, any one of slider controls 4418-4420 may beassigned to and vary corresponding control parameters used to setrelative gains (e.g., relative contributions) applied to one or morecomponents of an intermediary image resulting from true color processing(e.g., block 4324), and/or any other processing described herein (e.g.,block 4320 and/or various blocks of processes 4100 and/or 4200), andthereby vary their relative contribution in a user-displayed image.

In other embodiments, a user may use selectable text box 4422 to assignone or more control parameters, blending parameters, or other operatingparameters to any of slider controls 4418-4420 and/or knob/joystick4424, for example, to select multiple operating parameters foradjustment at substantially the same time. Embodiments of imagingsystems including a user interface similar to user interface 4400 maydisplay combined and/or other processed or unprocessed images adjustedfor a particular application and/or time-sensitive need. Such combinedimages may be used for enhanced infrared imaging applicationsbenefitting from increased scene detail, contrast, or other processingoperations disclosed herein.

Any of the various methods, processes, and/or operations describedherein may be performed by any of the various systems, devices, and/orcomponents described herein where appropriate.

For example, in various embodiments, imaging system 4000 in FIG. 24 maybe adapted to use a variety of the image processing methods disclosedherein to provide a variable imaging experience throughout a number ofdifferent environmental conditions, application needs, and/or userrequirements/expectations.

In one embodiment, imaging system 4000 may be adapted to select one ormore methodologies depending on available light.

When there is sufficient daylight, imaging system 4000 may be adapted toprovide combined images and/or video that include real-time visiblespectrum characteristics blended with and/or otherwise enhanced withreal-time infrared characteristics (e.g., visible spectrum images andinfrared images are captured substantially simultaneously and thenblended according to any one or combination of methods describedherein). For example, a luminosity component of visible spectrum imagesmay be blended with a radiometric luminosity component of correspondinginfrared images such that the brightness of visible spectrum color ismodulated according to a temperature of an object in the images (e.g.,true color processing, similar to that described in relation to block4324 and/or portions of process 4200). In other embodiments, any one orcombination of triple fusion, high contrast, and/or true colorprocessing may be used to generate such blended combined images (e.g.,blocks 4320-4326). In further embodiments (e.g., when there issufficient ambient light), imaging system 4000 may be adapted to providenon-infrared enhanced visible spectrum images and/or video (e.g.,without blending visible spectrum images with infrared images).

When there is an intermediate amount of daylight, such as at dusk, orwhen there are other environmental conditions reducing the availablelight, imaging system 4000 may be adapted to provide combined imagesand/or video that include real-time infrared characteristics blendedwith time-spaced visible spectrum characteristics (e.g., captured priorto the infrared imagery), as described herein, with real-time visiblespectrum characteristics (e.g., using available light), or a combinationof time-spaced and real-time visible spectrum characteristics. Forexample, a radiometric luminosity component of infrared images may beblended with a chrominance component of corresponding real-time and/ortime-spaced visible spectrum images such that the resulting combinedimages contain infrared imagery blended with accurate and/orsemi-accurate visible spectrum colors (e.g., block 4324 and/or portionsof process 4200). In another embodiment, a radiometric luminositycomponent of infrared images may be blended with high contrast contentderived from corresponding real-time and/or time-spaced visible spectrumimages (e.g., using high contrast processing, block 4326, portions ofprocess 4200, and/or as described herein) such that the resultingcombined images contain infrared imagery enhanced with edge and otherdetail typically unavailable in raw infrared imagery. In otherembodiments, any one or combination of triple fusion, high contrast,and/or true color processing may be used to generate such blendedcombined images (e.g., blocks 4320-4326).

When there is an very little or no daylight and/or ambient light, suchas at night, or when there are other environmental conditions reducingthe available light, imaging system 4000 may be adapted to providecombined images and/or video that include real-time infraredcharacteristics blended with time-spaced visible spectrumcharacteristics (e.g., captured prior to the infrared imagery), asdescribed herein. For example, a radiometric luminosity component ofinfrared images may be blended with a chrominance component ofcorresponding time-spaced visible spectrum images such that theresulting combined images contain infrared imagery blended withrepresentative visible spectrum colors (e.g., block 4324 and/or portionsof process 4200). In another embodiment, a radiometric luminositycomponent of infrared images may be blended with high contrast contentderived from corresponding time-spaced visible spectrum images (e.g.,using high contrast processing, block 4326, portions of process 4200,and/or as described herein) such that the resulting combined imagescontain infrared imagery enhanced with time-spaced edge and other detailtypically unavailable in raw infrared imagery. In other embodiments, anyone or combination of triple fusion, high contrast, and/or true colorprocessing may be used to generate such blended combined images (e.g.,blocks 4320-4326). In further embodiments (e.g., when there issubstantially no ambient light), imaging system 4000 may be adapted toprovide non-visible spectrum infrared images and/or video (e.g., withoutblending infrared images with visible spectrum images).

In various embodiments, imaging system 4000 may be adapted to select aparticular processing methodology or morph from one selection ofprocessing methodology to another based on a detected amount ofavailable ambient light and/or daylight (e.g., detected by visiblespectrum imaging module 4002 a, for example, or by one or more of othercomponents 4018). For example, different combined images resulting fromdifferent methodologies may be blended together according to a measureof available light in the combined images. In some embodiments, imagingsystem 4000 may be adapted to use different sets of processingmethodology with respect to different portions of captured imagesdepending on an amount of available ambient light and/or daylightdetected in the portions of the captured images. In further embodiments,imaging system 4000 may be adapted to select and/or morph variousprocessing methodologies according to a schedule, based on variousinformation (e.g., sensor data measured by image sensors 4002 a,b and/orvarious other components 4018), based on various processing operations(e.g., pre, post, high contrast, true color, NUC, triple fusion, timebased, and/or other types of processing described herein), and/or basedon other appropriate techniques. In such embodiments, imaging system4000 may be adapted to vary one or more processing methodologies withand/or without user input (e.g., in response to machine-based input), asdescribed herein.

Where applicable, various embodiments provided by the present disclosurecan be implemented using hardware, software, or combinations of hardwareand software. Also where applicable, the various hardware componentsand/or software components set forth herein can be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein can be separated into sub-components comprising software,hardware, or both without departing from the spirit of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components can be implemented as hardware components, andvice-versa.

Software in accordance with the present disclosure, such asnon-transitory instructions, program code, and/or data, can be stored onone or more non-transitory machine readable mediums. It is alsocontemplated that software identified herein can be implemented usingone or more general purpose or specific purpose computers and/orcomputer systems, networked and/or otherwise. Where applicable, theordering of various steps described herein can be changed, combined intocomposite steps, and/or separated into sub-steps to provide featuresdescribed herein.

Embodiments described above illustrate but do not limit the invention.It should also be understood that numerous modifications and variationsare possible in accordance with the principles of the invention.Accordingly, the scope of the invention is defined only by the followingclaims.

What is claimed is:
 1. A system comprising: a memory adapted to receivea visible spectrum image of a scene from a visible spectrum imager and aplurality of infrared images of the scene from an infrared imager; and aprocessor configured to communicate with the memory, wherein theprocessor is configured to: generate a blurred infrared image from atleast one of the plurality of infrared images; determine, for each rowof the blurred infrared image, a corresponding row fixed pattern noise(FPN) correction term; determine, for each column of the blurredinfrared image, a corresponding column FPN correction term; apply therow and column FPN correction terms to the blurred infrared image toprovide a row and column FPN corrected blurred infrared image; determinea plurality of non-uniformity correction (NUC) terms based, at least inpart, on the row and column FPN corrected blurred infrared image; applythe NUC terms to one of the plurality of the infrared images to removenoise from the one of the plurality of the infrared images to provide acorrected infrared image; receive control parameters; derive highspatial frequency content from at least one of the visible spectrumimage and the corrected infrared image; and generate a combined imagecomprising relative contributions of at least the high spatial frequencycontent, wherein the relative contributions are determined in real-timeby a user adjusting the control parameters.
 2. The system of claim 1,wherein the generating the blurred infrared image comprises: detectingmotion of the infrared imager based on motion sensor data provided by amotion sensor; receiving a set of the plurality of infrared in responseto detected motion; generating a blurred infrared image from the set ofthe plurality of infrared images based on an average of the set of theplurality of infrared images.
 3. The system of claim 1, wherein: theapplying the NUC terms to the one of the plurality of infrared imagescomprises selectively applying the NUC terms to the one of the pluralityof infrared images to selectively remove noise from the one of theplurality of infrared images; and the selectively applying the NUC termsis determined by the control parameters.
 4. The system of claim 1,further comprising the infrared imager, wherein the infrared imager isconfigured to: generate the blurred infrared image of the scene; andderive the plurality of NUC terms.
 5. The system of claim 1, wherein:one or more of the control parameters are determined by one or morethreshold values associated with at least one of the visible spectrumimage, the plurality of infrared images, and the corrected infraredimage.
 6. The system of claim 1, further comprising a user interfaceconfigured to communicate with the memory and the processor, wherein theprocessor is configured to: receive user input from the user interface;and determine one or more of the control parameters from the user input.7. The system of claim 1, further comprising deriving colorcharacteristics of the scene from at least one of the visible spectrumimage and the infrared image, wherein: the color characteristics of thescene are derived from a chrominance component of the visible spectrumimage; the relative contributions include a luminance component of thecorrected infrared image; and the relative contribution of the luminancecomponent of the corrected infrared image substantially matches therelative contribution of the color characteristics, as controlled by thecontrol parameters.
 8. The system of claim 7, wherein: the luminancecomponent of the corrected infrared image is blended with a luminancecomponent of the visible spectrum image before being contributed to thecombined image.
 9. The system of claim 1, wherein: the high spatialfrequency content is derived from a luminance component of the visiblespectrum image; the relative contributions include a chrominancecomponent of the corrected infrared image; and the relative contributionof the chrominance component of the corrected infrared imagesubstantially matches the relative contribution of the high spatialfrequency content, as controlled by the control parameters.
 10. Thesystem of claim 9, wherein: the high spatial frequency content isblended with a luminance component of the corrected infrared imagebefore being contributed to the combined image.
 11. The system of claim1, further comprising the infrared imager, wherein: the infrared imagercomprises a focal plane array (FPA) configured to capture the pluralityof infrared images of the scene; and the FPA comprises an array ofmicrobolometers adapted to receive a bias voltage selected from a rangeof approximately 0.2 volts to approximately 0.7 volts.
 12. A methodcomprising: receiving a visible spectrum image of a scene from a visiblespectrum imager and a plurality of infrared images of the scene from aninfrared imager; generating a blurred infrared image from at least oneof the plurality of infrared images; determining, for each row of theblurred infrared image, a corresponding row fixed pattern noise (FPN)correction term; determining, for each column of the blurred infraredimage, a corresponding column FPN correction term; applying the row andcolumn FPN correction terms to the blurred infrared image to provide arow and column FPN corrected blurred infrared image; determining aplurality of non-uniformity correction (NUC) terms based, at least inpart, on the row and column FPN corrected blurred infrared image;applying the NUC terms to one of the plurality of the infrared images toremove noise from the one of the plurality of the infrared images toprovide a corrected infrared image; receiving control parameters;deriving color characteristics of the scene from at least one of thevisible spectrum image and the corrected infrared image; and generatinga combined image comprising relative contributions of at least the colorcharacteristics, wherein the relative contributions are determined inreal-time by a user adjusting the control parameters.
 13. The method ofclaim 12, wherein the generating the blurred infrared image comprises:detecting motion of the infrared imager based on motion sensor dataprovided by a motion sensor; receiving a set of the plurality ofinfrared images in response to detected motion; and generating theblurred infrared image from the set of the plurality of infrared imagesbased on an average of the set of the plurality of infrared images. 14.The method of claim 12, wherein the applying the NUC terms to the one ofthe plurality of infrared images comprises selectively applying the NUCterms to the one of the plurality of infrared images.
 15. The method ofclaim 12, wherein the infrared imager is configured to: generate theblurred infrared image of the scene by intentionally blurring one of theplurality of infrared images of the scene or by determining a set of theplurality of infrared images corresponding to detected motion andgenerating the blurred infrared image from the set of the plurality ofinfrared images based on an average of the set of the plurality ofinfrared images; and determine the plurality of NUC terms.
 16. Themethod of claim 12, wherein: one or more of the control parameters aredetermined by one or more threshold value; associated with at least oneof the visible spectrum image, the plurality of infrared images, and thecorrected infrared image.
 17. The method of claim 12, furthercomprising: receiving user input from a user interface, wherein one ormore of the control parameters are determined by the user input.
 18. Themethod of claim 12, further comprising: receiving information from asensor, wherein one or more of the control parameters are determined bythe information, wherein the information is associated with an amount ofavailable ambient light.
 19. The method of claim 12, wherein: the colorcharacteristics of the scene are derived from a chrominance component ofthe visible spectrum image; the relative contributions include aluminance component of the corrected infrared image; and the relativecontribution of the luminance component of the corrected infrared imagesubstantially matches the relative contribution of the colorcharacteristics, as controlled by the control parameters.
 20. The methodof claim 19, wherein: the luminance component of the corrected infraredimage is blended with a luminance component of the visible spectrumimage before being contributed to the combined image.
 21. The method ofclaim 12, further comprising deriving high spatial frequency contentfrom at least one of the visible spectrum image and the infrared image,wherein: the high spatial frequency content is derived from a luminancecomponent of the visible spectrum image; the relative contributionsinclude a chrominance component of the corrected infrared image; and therelative contribution of the chrominance component of the correctedinfrared image substantially matches the relative contribution of thehigh spatial frequency content, as controlled by the control parameters.22. The method of claim 21, wherein: the high spatial frequency contentis blended with a luminance component of the corrected infrared imagebefore being contributed to the combined image.
 23. A non-transitorymachine-readable medium comprising a plurality of machine-readableinstructions which when executed by one or more processors of a systemare adapted to cause the system to perform a method comprising:receiving a visible spectrum image of a scene from a visible spectrumimager and a plurality of infrared images of the scene from an infraredimager; generating a blurred infrared image least one of the pluralityof infrared images; determining, for each row of the blurred infraredimage, a corresponding row fixed pattern noise (FPN) correction term;determining, for each column of the blurred infrared image, acorresponding column FPN correction term; applying the row and columnFPN correction terms to the blurred infrared image to provide a row andcolumn FPN corrected blurred infrared image; determining a plurality ofnon-uniformity correction (NUC) terms based, at least in part, on therow and column FPN corrected blurred infrared image; applying the NUCterms to one of the plurality of the infrared images to remove noisefrom the one of the plurality of the infrared images to provide acorrected infrared image; receiving control parameters; deriving highspatial frequency content from at least one of the visible spectrumimage and the corrected infrared image; and generating a combined imagecomprising relative contributions of at least the high spatial frequencycontent, wherein the relative contributions are determined in real-timeby a user adjusting the control parameters.
 24. The system of claim 2,further comprising the visible spectrum imager and the infrared imager,wherein the infrared imager comprises the motion sensor.